40,474 results
Search Results
2. CIRSE Position Paper on Artificial Intelligence in Interventional Radiology
- Author
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Najafi, Arash, Cazzato, Roberto Luigi, Meyer, Bernhard C., Pereira, Philippe L., Alberich, Angel, López, Antonio, Ronot, Maxime, Fritz, Jan, Maas, Monique, Benson, Sean, Haage, Patrick, and Gomez Munoz, Fernando
- Published
- 2023
- Full Text
- View/download PDF
3. Assessment of Published Papers on the Use of Machine Learning in Diagnosis and Treatment of Mastitis.
- Author
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Bourganou, Maria V., Kiouvrekis, Yiannis, Chatzopoulos, Dimitrios C., Zikas, Sotiris, Katsafadou, Angeliki I., Liagka, Dimitra V., Vasileiou, Natalia G. C., Fthenakis, George C., and Lianou, Daphne T.
- Subjects
MACHINE learning ,ARTIFICIAL intelligence ,SUPPORT vector machines ,COMPUTERS in agriculture ,MASTITIS - Abstract
The present study is an evaluation of published papers on machine learning as employed in mastitis research. The aim of this study was the quantitative evaluation of the scientific content and the bibliometric details of these papers. In total, 69 papers were found to combine machine learning in mastitis research and were considered in detail. There was a progressive yearly increase in published papers, which originated from 23 countries (mostly from China or the United States of America). Most original articles (n = 59) referred to work involving cattle, relevant to mastitis in individual animals. Most articles described work related to the development and diagnosis of the infection. Fewer articles described work on the antibiotic resistance of pathogens isolated from cases of mastitis and on the treatment of the infection. In most studies (98.5% of published papers), supervised machine learning models were employed. Most frequently, decision trees and support vector machines were employed in the studies described. 'Machine learning' and 'mastitis' were the most frequently used keywords. The papers were published in 39 journals, with most frequent publications in Computers and Electronics in Agriculture and Journal of Dairy Science. The median number of cited references in the papers was 39 (interquartile range: 31). There were 435 co-authors in the papers (mean: 6.2 per paper, median: 5, min.–max.: 1–93) and 356 individual authors. The median number of citations received by the papers was 4 (min.–max.: 0–70). Most papers (72.5%) were published in open-access mode. This study summarized the characteristics of papers on mastitis and artificial intelligence. Future studies could explore using these methodologies at farm level, and extending them to other animal species, while unsupervised learning techniques might also prove to be useful. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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4. How Artificial Intelligence will Reshape our Interventional Units.
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Boeken T, Dean C, Pellerin O, and Sapoval M
- Subjects
- Humans, Artificial Intelligence, Machine Learning
- Published
- 2024
- Full Text
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5. Research Paper Screening Tool: Automating Conference Paper Evaluation and Enhancement.
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Rathnasiri, Hansani Upeksha, Ishara Lakshani, L. A., Amarasinghe, Nipuni Nilakna, Dissanayake, Oshan Asinda, Nawinna, Dasuni, and Attanayaka, Buddima
- Subjects
TECHNOLOGICAL innovations ,ARTIFICIAL neural networks ,MACHINE learning ,ARTIFICIAL intelligence ,NATURAL language processing - Abstract
In this era of knowledge, academic researchers are growing every day, this also spikes a growth in published literature on the new innovations and findings. This leads to a problem where the reviewers at the conferences must go through many research papers to determine whether they are suitable for the conference or not. This problem has caused the necessity of an effective paper screening tool for optimizing the literature review process. This research presents a development of a new Paper Screening Tool (PST) aimed at increasing the efficiency and accuracy of the literature screening phase. Leveraging the NPL processing techniques this PST and reduces a lot of manual efforts. Through comprehensive evaluation using a diverse dataset, the tools provide high precision. The PST also has user friendly interfaces and customizable report generation which empowers the researchers screening process to their specific needs. This paper contributes to literature by solving the challenge of information overloading during the literature review. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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6. Assessment of Published Papers on the Use of Machine Learning in Diagnosis and Treatment of Mastitis
- Author
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Maria V. Bourganou, Yiannis Kiouvrekis, Dimitrios C. Chatzopoulos, Sotiris Zikas, Angeliki I. Katsafadou, Dimitra V. Liagka, Natalia G. C. Vasileiou, George C. Fthenakis, and Daphne T. Lianou
- Subjects
algorithm ,artificial intelligence ,cattle ,machine learning ,mammary infection ,mastitis ,Information technology ,T58.5-58.64 - Abstract
The present study is an evaluation of published papers on machine learning as employed in mastitis research. The aim of this study was the quantitative evaluation of the scientific content and the bibliometric details of these papers. In total, 69 papers were found to combine machine learning in mastitis research and were considered in detail. There was a progressive yearly increase in published papers, which originated from 23 countries (mostly from China or the United States of America). Most original articles (n = 59) referred to work involving cattle, relevant to mastitis in individual animals. Most articles described work related to the development and diagnosis of the infection. Fewer articles described work on the antibiotic resistance of pathogens isolated from cases of mastitis and on the treatment of the infection. In most studies (98.5% of published papers), supervised machine learning models were employed. Most frequently, decision trees and support vector machines were employed in the studies described. ‘Machine learning’ and ‘mastitis’ were the most frequently used keywords. The papers were published in 39 journals, with most frequent publications in Computers and Electronics in Agriculture and Journal of Dairy Science. The median number of cited references in the papers was 39 (interquartile range: 31). There were 435 co-authors in the papers (mean: 6.2 per paper, median: 5, min.–max.: 1–93) and 356 individual authors. The median number of citations received by the papers was 4 (min.–max.: 0–70). Most papers (72.5%) were published in open-access mode. This study summarized the characteristics of papers on mastitis and artificial intelligence. Future studies could explore using these methodologies at farm level, and extending them to other animal species, while unsupervised learning techniques might also prove to be useful.
- Published
- 2024
- Full Text
- View/download PDF
7. Artificial intelligence in veterinary diagnostic imaging: A literature review.
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Hennessey E, DiFazio M, Hennessey R, and Cassel N
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- Animals, Humans, Radiologists, Diagnostic Imaging, Artificial Intelligence, Machine Learning
- Abstract
Artificial intelligence in veterinary medicine is an emerging field. Machine learning, a subfield of artificial intelligence, allows computer programs to analyze large imaging datasets and learn to perform tasks relevant to veterinary diagnostic imaging. This review summarizes the small, yet growing body of artificial intelligence literature in veterinary imaging, provides necessary background to understand these papers, and provides author commentary on the state of the field. To date, less than 40 peer-reviewed publications have utilized machine learning to perform imaging-associated tasks across multiple anatomic regions in veterinary clinical and biomedical research. Major challenges in this field include collection and cleaning of sufficient image data, selection of high-quality ground truth labels, formation of relationships between veterinary and machine learning professionals, and closure of the gap between academic uses of artificial intelligence and currently available commercial products. Further development of artificial intelligence has the potential to help meet the growing need for radiological services through applications in workflow, quality control, and image interpretation for both general practitioners and radiologists., (© 2022 American College of Veterinary Radiology.)
- Published
- 2022
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8. Detection of fake papers in the era of artificial intelligence.
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Dadkhah, Mehdi, Oermann, Marilyn H., Hegedüs, Mihály, Raman, Raghu, and Dávid, Lóránt Dénes
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- *
ARTIFICIAL intelligence , *MACHINE learning , *DECISION trees , *PAPER mills - Abstract
Paper mills, companies that write scientific papers and gain acceptance for them, then sell authorships of these papers, present a key challenge in medicine and other healthcare fields. This challenge is becoming more acute with artificial intelligence (AI), where AI writes the manuscripts and then the paper mills sell the authorships of these papers. The aim of the current research is to provide a method for detecting fake papers. The method reported in this article uses a machine learning approach to create decision trees to identify fake papers. The data were collected from Web of Science and multiple journals in various fields. The article presents a method to identify fake papers based on the results of decision trees. Use of this method in a case study indicated its effectiveness in identifying a fake paper. This method to identify fake papers is applicable for authors, editors, and publishers across fields to investigate a single paper or to conduct an analysis of a group of manuscripts. Clinicians and others can use this method to evaluate articles they find in a search to ensure they are not fake articles and instead report actual research that was peer reviewed prior to publication in a journal. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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9. The 100 most influential papers in medical artificial intelligence; a bibliometric analysis
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Fatima Zahoor, Muhammad Abdullah, Muhammad Waleed Tahir, and Asif Islam
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Artificial intelligence ,Machine learning ,Computer reasoning ,Machine intelligence ,Medicine - Abstract
Objective: To assess the current trends in the field of artificial intelligence in medicine by analysing 100 most cited original articles relevant to the field. Methods: The systematic review was conducted in September 2022, and comprised literature search on Scopus database for original articles only. Google and Medical Subject Headings databases were used as resources to extract key words. In order to cover a broad range of articles, original studies comprising human as well as non-human subjects, studies without abstract and studies in languages other than English were part of the inclusion criteria. There was no specific time period applied to the search and no specific selection was done regarding the journals in the database. The screening was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to extract the top 100 most cited articles in the field of artificial intelligence usage in medicine. Data was analysed using SPSS 23. Results: Of the 11,571 studies identified, 100(0.86%) were analysed in detail. The studies were published between 1986 and 2021, with a median of 43 citations (IQR 53) per article. The journal ‘Artificial Intelligence in Medicine’ accounted for the highest number 9(9%)) of articles, and the United States was the country of origin for most of the articles 36(36%). Conclusion: The trends, development and shortcomings in field of artificial intelligence usage in medicine need to be understood to conduct an effective research in areas that still need attention, and to guide the authorities to direct their funding accordingly.
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- 2024
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10. Artificial Intelligence in Quantitative Ultrasound Imaging: A Survey.
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Zhou B, Yang X, Curran WJ, and Liu T
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- Humans, Tomography, X-Ray Computed, Ultrasonography, Workflow, Artificial Intelligence, Machine Learning
- Abstract
Quantitative ultrasound (QUS) imaging is a safe, reliable, inexpensive, and real-time technique to extract physically descriptive parameters for assessing pathologies. Compared with other major imaging modalities such as computed tomography and magnetic resonance imaging, QUS suffers from several major drawbacks: poor image quality and inter- and intra-observer variability. Therefore, there is a great need to develop automated methods to improve the image quality of QUS. In recent years, there has been increasing interest in artificial intelligence (AI) applications in medical imaging, and a large number of research studies in AI in QUS have been conducted. The purpose of this review is to describe and categorize recent research into AI applications in QUS. We first introduce the AI workflow and then discuss the various AI applications in QUS. Finally, challenges and future potential AI applications in QUS are discussed., (© 2021 American Institute of Ultrasound in Medicine.)
- Published
- 2022
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11. The Future of Heritage Science and Technologies: Papers from Florence Heri-Tech 2022.
- Author
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Furferi, Rocco, Colombini, Maria Perla, Seymour, Kate, Pelagotti, Anna, and Gherardini, Francesco
- Subjects
- *
GEOGRAPHIC information systems , *SCIENTIFIC literature , *SPECTRAL imaging , *ARTIFICIAL intelligence , *ULTRASONIC testing , *WORLD Heritage Sites , *MACHINE learning - Abstract
The article discusses the potential of advanced technologies in the field of cultural heritage. It highlights how these technologies, such as virtual reality, artificial intelligence, and 3D printing, can be used to understand, preserve, and enhance cultural heritage. The article also presents scientific papers from the Florence Heri-Tech International Conference, showcasing the various applications of these technologies. The papers cover topics such as the use of hyperspectral imaging for hieroglyph recognition, the enhancement of user experience in cultural spaces through advanced systems, and the use of non-invasive techniques for conservation. Overall, the article emphasizes the significant impact of technology on the research, preservation, and promotion of cultural heritage. [Extracted from the article]
- Published
- 2024
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12. A framework for rigorous evaluation of human performance in human and machine learning comparison studies.
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Cowley HP, Natter M, Gray-Roncal K, Rhodes RE, Johnson EC, Drenkow N, Shead TM, Chance FS, Wester B, and Gray-Roncal W
- Subjects
- Algorithms, Humans, Reproducibility of Results, Artificial Intelligence, Machine Learning
- Abstract
Rigorous comparisons of human and machine learning algorithm performance on the same task help to support accurate claims about algorithm success rates and advances understanding of their performance relative to that of human performers. In turn, these comparisons are critical for supporting advances in artificial intelligence. However, the machine learning community has lacked a standardized, consensus framework for performing the evaluations of human performance necessary for comparison. We demonstrate common pitfalls in a designing the human performance evaluation and propose a framework for the evaluation of human performance, illustrating guiding principles for a successful comparison. These principles are first, to design the human evaluation with an understanding of the differences between human and algorithm cognition; second, to match trials between human participants and the algorithm evaluation, and third, to employ best practices for psychology research studies, such as the collection and analysis of supplementary and subjective data and adhering to ethical review protocols. We demonstrate our framework's utility for designing a study to evaluate human performance on a one-shot learning task. Adoption of this common framework may provide a standard approach to evaluate algorithm performance and aid in the reproducibility of comparisons between human and machine learning algorithm performance., (© 2022. The Author(s).)
- Published
- 2022
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13. Machine learning in pharmacometrics: Opportunities and challenges.
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McComb M, Bies R, and Ramanathan M
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- Algorithms, Big Data, Humans, Pharmaceutical Preparations, Artificial Intelligence, Machine Learning
- Abstract
The explosive growth in medical devices, imaging and diagnostics, computing, and communication and information technologies in drug development and healthcare has created an ever-expanding data landscape that the pharmacometrics (PMX) research community must now traverse. The tools of machine learning (ML) have emerged as a powerful computational approach in other data-rich disciplines but its effective utilization in the pharmaceutical sciences and PMX modelling is in its infancy. ML-based methods can complement PMX modelling by enabling the information in diverse sources of big data, e.g. population-based public databases and disease-specific clinical registries, to be harnessed because they are capable of efficiently identifying salient variables associated with outcomes and delineating their interdependencies. ML algorithms are computationally efficient, have strong predictive capabilities and can enable learning in the big data setting. ML algorithms can be viewed as providing a computational bridge from big data to complement PMX modelling. This review provides an overview of the strengths and weaknesses of ML approaches vis-à-vis population methods, assesses current research into ML applications in the pharmaceutical sciences and provides perspective for potential opportunities and strategies for the successful integration and utilization of ML in PMX., (© 2021 British Pharmacological Society.)
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- 2022
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14. Machine learning models to support chemical R&D recognised with Best Paper Award
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Artificial intelligence ,Machine learning ,Artificial intelligence ,Business, international ,University of London. Imperial College of Science and Technology - Abstract
London: Imperial College London, South Kensington Campus has issued the following news release: A team from Imperial and BASF has won the Computers & Chemical Engineering Best Paper Award 2023 [...]
- Published
- 2024
15. The role of machine learning in clinical research: transforming the future of evidence generation.
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Weissler EH, Naumann T, Andersson T, Ranganath R, Elemento O, Luo Y, Freitag DF, Benoit J, Hughes MC, Khan F, Slater P, Shameer K, Roe M, Hutchison E, Kollins SH, Broedl U, Meng Z, Wong JL, Curtis L, Huang E, and Ghassemi M
- Subjects
- Humans, United States, United States Food and Drug Administration, Artificial Intelligence, Machine Learning
- Abstract
Background: Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the current and future state of ML for clinical research. Key areas of clinical trial methodology in which ML holds particular promise and priority areas for further investigation are presented alongside a narrative review of evidence supporting the use of ML across the clinical trial spectrum., Results: Conference attendees included stakeholders, such as biomedical and ML researchers, representatives from the US Food and Drug Administration (FDA), artificial intelligence technology and data analytics companies, non-profit organizations, patient advocacy groups, and pharmaceutical companies. ML contributions to clinical research were highlighted in the pre-trial phase, cohort selection and participant management, and data collection and analysis. A particular focus was paid to the operational and philosophical barriers to ML in clinical research. Peer-reviewed evidence was noted to be lacking in several areas., Conclusions: ML holds great promise for improving the efficiency and quality of clinical research, but substantial barriers remain, the surmounting of which will require addressing significant gaps in evidence., (© 2021. The Author(s).)
- Published
- 2021
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16. Conceptual Modeling and Artificial Intelligence: Challenges and Opportunities for Enterprise Engineering : Keynote Presentation at the 11th Enterprise Engineering Working Conference (EEWC 2021)
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Bork, Dominik, van der Aalst, Wil, Series Editor, Mylopoulos, John, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Aveiro, David, editor, Proper, Henderik A., editor, Guerreiro, Sérgio, editor, and de Vries, Marne, editor
- Published
- 2022
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17. Phishing Website Detection Using Several Machine Learning Algorithms: A Review Paper
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Alexander M. Veach and Munther Abualkibash
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artificial intelligence ,data science ,machine learning ,phishing ,Information technology ,T58.5-58.64 ,Computer engineering. Computer hardware ,TK7885-7895 - Abstract
Phishing is one of the major web social engineering attacks. This has led to demand for a better way to predict and stop them in a commercial environment. This paper seeks to understand the research done in the field and analyse the next steps forward. This is done by focusing on what goes into the selection of proper features, from manual selection to the use of Genetic Algorithms such as ADABoost and MultiBoost. Then a look into the classifiers in use, Neural Networks and Ensemble algorithms which were prominent alongside some novel approaches. This information is then processed into a framework for cloud-based and client-based phishing website detection, alongside suggestions for possible future research and experiments that could help progress the field.
- Published
- 2022
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18. Triple Diamond Design Process : Human-centered Design for Data-Driven Innovation
- Author
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Schleith, Johannes, Tsar, Daniella, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Kurosu, Masaaki, editor, Yamamoto, Sakae, editor, Mori, Hirohiko, editor, Soares, Marcelo M., editor, Rosenzweig, Elizabeth, editor, Marcus, Aaron, editor, Rau, Pei-Luen Patrick, editor, Harris, Don, editor, and Li, Wen-Chin, editor
- Published
- 2022
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19. How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts
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Kocak, Burak, Kus, Ece Ates, and Kilickesmez, Ozgur
- Published
- 2021
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20. Artificial Intelligence and Machine Learning in Patient Blood Management: A Scoping Review.
- Author
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Meier JM and Tschoellitsch T
- Subjects
- Clinical Decision-Making, Humans, Artificial Intelligence, Machine Learning
- Abstract
Machine learning (ML) and artificial intelligence (AI) are widely used in many different fields of modern medicine. This narrative review gives, in the first part, a brief overview of the methods of ML and AI used in patient blood management (PBM) and, in the second part, aims at describing which fields have been analyzed using these methods so far. A total of 442 articles were identified by a literature search, and 47 of them were judged as qualified articles that applied ML and AI techniques in PBM. We assembled the eligible articles to provide insights into the areas of application, quality measures of these studies, and treatment outcomes that can pave the way for further adoption of this promising technology and its possible use in routine clinical decision making. The topics that have been investigated most often were the prediction of transfusion (30%), bleeding (28%), and laboratory studies (15%). Although in the last 3 years a constantly increasing number of questions of ML in PBM have been investigated, there is a vast scientific potential for further application of ML and AI in other fields of PBM., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2022 International Anesthesia Research Society.)
- Published
- 2022
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21. There Can Be no Other Reason for this Behavior: Issues in the Ascription of Knowledge to Humans and AI.
- Author
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Byers P
- Subjects
- Humans, Knowledge, Artificial Intelligence, Machine Learning
- Abstract
While machine learning techniques have been used to model categorization/decision making tasks that are beyond the capabilities of traditional AI, these new models are typically uninterpretable, i.e., the reasons for their decisions are not clear. Some have argued that, in developing machines that can report the reasons for their decisions, developers should take, as a guide, human explanations for behavior, which make reference to mental states (e.g., knowledge/belief). This proposal is correct, but unattainable given certain characteristics of current AI. To explain, this article draws on recent discourse-analytic research showing that ascriptions of knowledge/belief presume behavioral performances to instantiate particular sorts of broader dispositions. This is reflected by the possibility of ascribing knowledge/belief to an agent on the basis that there can be no other explanation for their observed behavior. The behavior of AI trained through machine learning is unpredictable in ways that precludes such certainty. Consequently, while it is certainly possible to program machines to report mental states of knowledge/belief to account for their decisions, the failure of current AI to engage in typically human forms of life means that such ascribed mental states are inevitably meaningless., (© 2020. Springer Science+Business Media, LLC, part of Springer Nature.)
- Published
- 2022
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22. A new active learning approach for adsorbate-substrate structural elucidation in silico.
- Author
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Lourenço MP, Herrera LB, Hostaš J, Calaminici P, Köster AM, Tchagang A, and Salahub DR
- Subjects
- Adsorption, Neural Networks, Computer, Software, Artificial Intelligence, Machine Learning
- Abstract
Adsorbate interactions with substrates (e.g. surfaces and nanoparticles) are fundamental for several technologies, such as functional materials, supramolecular chemistry, and solvent interactions. However, modeling these kinds of systems in silico, such as finding the optimum adsorption geometry and energy, is challenging, due to the huge number of possibilities of assembling the adsorbate on the surface. In the current work, we have developed an artificial intelligence (AI) approach based on an active learning (AL) method for adsorption optimization on the surface of materials. AL uses machine learning (ML) regression algorithms and their uncertainties to make a decision (based on a policy) for the next unexplored structures to be computed, increasing, though, the probability of finding the global minimum with a small number of calculations. The methodology allows an accurate and automated structural elucidation of the adsorbate on the surface, based on the minimization of the total electronic energy. The new AL method for adsorption optimization was developed and implemented in the quantum machine learning software/agent for material design and discovery (QMLMaterial) program and was applied for C
60 @TiO2 anatase (101). It marks another software extension with a new feature in addition to the automatic structural elucidation of defects in materials and of nanoparticles as well. SCC-DFTB calculations were used to build the complex search surfaces with a reasonably low computational cost. An artificial neural network (NN) was employed in the AL framework evaluated together with two uncertainty quantification methods: K-fold cross-validation and non-parametric bootstrap (BS) resampling. Also, two different acquisition functions for decision-making were used: expected improvement (EI) and the lower confidence bound (LCB)., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)- Published
- 2022
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23. Machine Learning for Analysis of Microscopy Images: A Practical Guide.
- Author
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Zinchuk V and Grossenbacher-Zinchuk O
- Subjects
- Humans, Artificial Intelligence, Image Processing, Computer-Assisted methods, Machine Learning, Microscopy methods
- Abstract
The explosive growth of machine learning has provided scientists with insights into data in ways unattainable using prior research techniques. It has allowed the detection of biological features that were previously unrecognized and overlooked. However, because machine-learning methodology originates from informatics, many cell biology labs have experienced difficulties in implementing this approach. In this article, we target the rapidly expanding audience of cell and molecular biologists interested in exploiting machine learning for analysis of their research. We discuss the advantages of employing machine learning with microscopy approaches and describe the machine-learning pipeline. We also give practical guidelines for building models of cell behavior using machine learning. We conclude with an overview of the tools required for model creation, and share advice on their use. © 2020 by John Wiley & Sons, Inc., (© 2020 John Wiley & Sons, Inc.)
- Published
- 2020
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24. Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper.
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Vinodkumar, Prasoon Kumar, Karabulut, Dogus, Avots, Egils, Ozcinar, Cagri, and Anbarjafari, Gholamreza
- Subjects
- *
DEEP learning , *COMPUTER vision , *GRAPH neural networks , *ARTIFICIAL intelligence , *MACHINE learning , *GENERATIVE adversarial networks - Abstract
The research groups in computer vision, graphics, and machine learning have dedicated a substantial amount of attention to the areas of 3D object reconstruction, augmentation, and registration. Deep learning is the predominant method used in artificial intelligence for addressing computer vision challenges. However, deep learning on three-dimensional data presents distinct obstacles and is now in its nascent phase. There have been significant advancements in deep learning specifically for three-dimensional data, offering a range of ways to address these issues. This study offers a comprehensive examination of the latest advancements in deep learning methodologies. We examine many benchmark models for the tasks of 3D object registration, augmentation, and reconstruction. We thoroughly analyse their architectures, advantages, and constraints. In summary, this report provides a comprehensive overview of recent advancements in three-dimensional deep learning and highlights unresolved research areas that will need to be addressed in the future. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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25. Genome-Enabled Prediction Methods Based on Machine Learning.
- Author
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Reinoso-Peláez EL, Gianola D, and González-Recio O
- Subjects
- Algorithms, Animals, Bayes Theorem, Genome, Artificial Intelligence, Machine Learning
- Abstract
Growth of artificial intelligence and machine learning (ML) methodology has been explosive in recent years. In this class of procedures, computers get knowledge from sets of experiences and provide forecasts or classification. In genome-wide based prediction (GWP), many ML studies have been carried out. This chapter provides a description of main semiparametric and nonparametric algorithms used in GWP in animals and plants. Thirty-four ML comparative studies conducted in the last decade were used to develop a meta-analysis through a Thurstonian model, to evaluate algorithms with the best predictive qualities. It was found that some kernel, Bayesian, and ensemble methods displayed greater robustness and predictive ability. However, the type of study and data distribution must be considered in order to choose the most appropriate model for a given problem., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)
- Published
- 2022
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26. Agents in Traffic and Transportation (ATT 2022): Revised and Extended Papers.
- Author
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Bazzan, Ana L.C., Dusparic, Ivana, Lujak, Marin, and Vizzari, Giuseppe
- Subjects
- *
ARTIFICIAL intelligence , *MACHINE learning , *DATA structures , *REINFORCEMENT learning , *INTELLIGENT agents , *ADAPTIVE control systems - Abstract
This document discusses the societal impact of applying artificial intelligence (AI) to traffic and transportation. Sustainable transport is considered crucial for sustainable cities and communities, and AI has the potential to significantly influence daily life in urban areas. The document highlights the interdisciplinary challenges of understanding and coordinating transportation systems, which involve various players with conflicting goals and constraints. It also presents a selection of innovative solutions related to traffic management, including machine learning, reinforcement learning, and optimization approaches. The document concludes by expressing gratitude to the authors, reviewers, and committee members involved in the creation of the special issue. [Extracted from the article]
- Published
- 2024
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27. Assessing the Feasibility of Processing a Paper-based Multilingual Social Needs Screening Questionnaire Using Artificial Intelligence
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Artificial intelligence ,Natural language interfaces ,Medical records ,Computational linguistics ,Language processing ,Machine learning ,Artificial intelligence ,Computers - Abstract
2024 APR 16 (VerticalNews) -- By a News Reporter-Staff News Editor at Information Technology Newsweekly -- According to news reporting based on a preprint abstract, our journalists obtained the following [...]
- Published
- 2024
28. Textual features of peer review predict top-cited papers: An interpretable machine learning perspective.
- Author
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Sun, Zhuanlan
- Subjects
MACHINE learning ,ARTIFICIAL intelligence ,TECHNICAL reports ,PEER communication ,THEATER reviews ,EMOTION recognition - Abstract
• A framework combining machine learning models and SHAP to interpret how peer review improves the research impact. • The importance of key linguistic, sentiment, and peer review features from peer review reports in determining the scientific significance of papers. • Valuable insights for authors to improve the quality of work and increase academic influence by paying closer attention to peer review characteristics. • Textual features of peer review reports play an important role in predicting post-publication scientific impact. Peer review is crucial in improving the quality and reliability of scientific research. However, the mechanisms through which peer review practices ensure papers become top-cited papers (TCPs) after publication are not well understood. In this study, by collecting a data set containing 13, 066 papers published between 2016 and 2020 from Nature communications with open peer review reports, we aim to examine how textual features embedded within the peer review reports of papers that reflect the reviewers' emotions may predict the papers to be TCPs. We compiled a list of 15 textual features and classified them into three categories: peer review features, linguistic features, and sentiment features. We then chose the XGBoost machine learning model with the best performance in predicting TCPs, and utilized the explainable artificial intelligence techniques SHAP to interpret the role of feature importance on the prediction results. The distribution of feature importance ranking results demonstrates that sentiment features play a crucial role in determining papers' potential to be highly cited. This conclusion still holds, even when the ranking of the feature importance changes in the subgroup analysis of dividing the samples into four disciplines (biological sciences, health sciences, physical sciences, and earth and environmental sciences), as well as two groups based on whether reviewers' identities were revealed. This research emphasizes the textual features retrieved from peer review reports that play role in improving manuscript quality can predict the post-publication research impact. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
29. Machine learning and explainable artificial intelligence for the prevention of waterborne cryptosporidiosis and giardiosis.
- Author
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Ligda P, Mittas N, Kyzas GZ, Claerebout E, and Sotiraki S
- Subjects
- Humans, Oocysts, Waterborne Diseases prevention & control, Machine Learning, Cryptosporidiosis prevention & control, Cryptosporidiosis epidemiology, Cryptosporidium isolation & purification, Artificial Intelligence, Giardia isolation & purification, Giardiasis prevention & control, Giardiasis epidemiology
- Abstract
Cryptosporidium and Giardia are important parasitic protozoa due to their zoonotic potential and impact on human health, and have often caused waterborne outbreaks of disease. Detection of (oo)cysts in water matrices is challenging and extremely costly, thus only few countries have legislated for regular monitoring of drinking water for their presence. Several attempts have been made trying to investigate the association between the presence of such (oo)cysts in waters with other biotic or abiotic factors, with inconclusive findings. In this regard, the aim of this study was the development of an holistic approach leveraging Machine Learning (ML) and eXplainable Artificial Intelligence (XAI) techniques, in order to provide empirical evidence related to the presence and prediction of Cryptosporidium oocysts and Giardia cysts in water samples. To meet this objective, we initially modelled the complex relationship between Cryptosporidium and Giardia (oo)cysts and a set of parasitological, microbiological, physicochemical and meteorological parameters via a model-agnostic meta-learner algorithm that provides flexibility regarding the selection of the ML model executing the fitting task. Based on this generic approach, a set of four well-known ML candidates were, empirically, evaluated in terms of their predictive capabilities. Then, the best-performed algorithms, were further examined through XAI techniques for gaining meaningful insights related to the explainability and interpretability of the derived solutions. The findings reveal that the Random Forest achieves the highest prediction performance when the objective is the prediction of both contamination and contamination intensity with Cryptosporidium oocysts in a given water sample, with meteorological/physicochemical and microbiological markers being informative, respectively. For the prediction of contamination with Giardia, the eXtreme Gradient Boosting with physicochemical parameters was the most efficient algorithm, while, the Support Vector Regression that takes into consideration both microbiological and meteorological markers was more efficient for evaluating the contamination intensity with cysts. The results of the study designate that the adoption of ML and XAI approaches can be considered as a valuable tool for unveiling the complicated correlation of the presence and contamination intensity with these zoonotic parasites that could constitute, in turn, a basis for the development of monitoring platforms and early warning systems for the prevention of waterborne disease outbreaks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)
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- 2024
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30. Harnessing computational tools of the digital era for enhanced infection control.
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Branda F
- Subjects
- Humans, Big Data, Artificial Intelligence, Infection Control methods, Machine Learning
- Abstract
This paper explores the potential of artificial intelligence, machine learning, and big data analytics in revolutionizing infection control. It addresses the challenges and innovative approaches in combating infectious diseases and antimicrobial resistance, emphasizing the critical role of interdisciplinary collaboration, ethical data practices, and integration of advanced computational tools in modern healthcare., (© 2024. The Author(s).)
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- 2024
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31. The role of AI and machine learning in the diagnosis of Parkinson's disease and atypical parkinsonisms.
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Dennis AP and Strafella AP
- Subjects
- Humans, Parkinsonian Disorders diagnosis, Neural Networks, Computer, Parkinson Disease diagnosis, Machine Learning, Artificial Intelligence
- Abstract
Parkinson's disease is a neurodegenerative movement disorder associated with motor and non-motor symptoms causing severe disability as the disease progresses. The development of biomarkers for Parkinson's disease to diagnose patients earlier and predict disease progression is imperative. As artificial intelligence and machine learning techniques efficiently process data and can handle multiple data types, we reviewed the literature to determine the extent to which these techniques have been applied to biomarkers for Parkinson's disease and movement disorders. We determined that the most applicable machine learning techniques are support vector machines and neural networks, depending on the size and type of the data being analyzed. Additionally, more complex machine learning techniques showed increased accuracy when compared to less complex techniques, especially when multiple machine learning models were combined. We can conclude that artificial intelligence and machine learning techniques may have the capacity to significantly boost diagnostic capacity in movement disorders and Parkinson's disease., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
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- 2024
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32. An analytical review on the use of artificial intelligence and machine learning in diagnosis, prediction, and risk factor analysis of multiple sclerosis.
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Pilehvari S, Morgan Y, and Peng W
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- Humans, Risk Factors, Multiple Sclerosis diagnosis, Multiple Sclerosis diagnostic imaging, Machine Learning, Artificial Intelligence
- Abstract
Medical research offers potential for disease prediction, like Multiple Sclerosis (MS). This neurological disorder damages nerve cell sheaths, with treatments focusing on symptom relief. Manual MS detection is time-consuming and error prone. Though MS lesion detection has been studied, limited attention has been paid to clinical analysis and computational risk factor prediction. Artificial intelligence (AI) techniques and Machine Learning (ML) methods offer accurate and effective alternatives to mapping MS progression. However, there are challenges in accessing clinical data and interdisciplinary collaboration. By analyzing 103 papers, we recognize the trends, strengths and weaknesses of AI, ML, and statistical methods applied to MS diagnosis. AI/ML-based approaches are suggested to identify MS risk factors, select significant MS features, and improve the diagnostic accuracy, such as Rule-based Fuzzy Logic (RBFL), Adaptive Fuzzy Inference System (ANFIS), Artificial Neural Network methods (ANN), Support Vector Machine (SVM), and Bayesian Networks (BNs). Meanwhile, applications of the Expanded Disability Status Scale (EDSS) and Magnetic Resonance Imaging (MRI) can enhance MS diagnostic accuracy. By examining established risk factors like obesity, smoking, and education, some research tackled the issue of disease progression. The performance metrics varied across different aspects of MS studies: Diagnosis: Sensitivity ranged from 60 % to 98 %, specificity from 60 % to 98 %, and accuracy from 61 % to 97 %. Prediction: Sensitivity ranged from 76 % to 98 %, specificity from 65 % to 98 %, and accuracy from 62 % to 99 %. Segmentation: Accuracy ranged up to 96.7 %. Classification: Sensitivity ranged from 78 % to 97.34 %, specificity from 65 % to 99.32 %, and accuracy from 71 % to 97.94 %. Furthermore, the literature shows that combining techniques can improve efficiency, exploiting their strengths for better overall performance., Competing Interests: Declaration of competing interest The authors have no conflicts of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)
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- 2024
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33. Healthcare LLMs Go to Market: A Realist Review of Product Launch News.
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Sharifi S, Namvar M, Intezari A, and Akhlaghpour S
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- Humans, Delivery of Health Care, Artificial Intelligence, Machine Learning, Digital Health
- Abstract
We provide a realist review of product launches for Large Language Models (LLMs) in the healthcare industry. Through a systematic search in the Factiva database and the application of a Context, Intervention, Mechanism, Outcome (CIMO) framework, we identified and assessed 23 significant records, representing 17 unique product launches between January 2023 and February 2024. This manuscript contributes to the emerging literature on health LLMs and Generative AI by focusing on actual product launches of healthcare LLM products-a less explored aspect than theoretical potential. Our use of the CIMO framework to dissect the application of LLMs in healthcare adds a fresh perspective to the discourse, helping to understand the outcomes and the mechanisms driving these outcomes. Among the LLM application themes that emerged from our review, we focused on four primary themes: Clinical Care and Health Services, Healthcare Documentation and Data Management, Insurance and Healthcare Financial Services, and Nutrition, Wellness, and Chronic Disease Management. Our findings demonstrate LLMs' potential to transform patient care through personalization and efficiency, highlighting their role in enhancing healthcare delivery systems, reducing administrative burdens, and supporting decision-making processes. Specific implementations by health start-ups and large tech firms discussed in this paper underscore the immediate impact of these technologies on patient care and healthcare management. This realist model offers a new perspective on LLMs within healthcare, providing an empirical basis for future technological integration and policy development in digital health. Our study contributes to understanding how LLMs operate within the healthcare sector, emphasizing the importance of context in their successful deployment and serving as a strategic guide for future AI integration in sensitive healthcare services.
- Published
- 2024
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34. Artificial intelligence and machine learning applications in urinary tract infections identification and prediction: a systematic review and meta-analysis.
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Shen L, An J, Wang N, Wu J, Yao J, and Gao Y
- Subjects
- Humans, Predictive Value of Tests, Urinary Tract Infections diagnosis, Machine Learning, Artificial Intelligence
- Abstract
Background: Urinary tract infections (UTIs) have been one of the most common bacterial infections in clinical practice worldwide. Artificial intelligence (AI) and machine learning (ML) based algorithms have been increasingly applied in UTI case identification and prediction. However, the overall performance of AI/ML algorithms in identifying and predicting UTI has not been evaluated. The purpose of this paper is to quantitatively evaluate the application value of AI/ML in identifying and predicting UTI cases., Methods: MEDLINE, EMBASE, Web of Science, and PubMed databases were systematically searched for articles published up to December 31, 2023. Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2) and Prediction Model Risk of Bias Assessment Tool (PROBAST) were used to assess the risk of bias. Study characteristics and detailed algorithm information were extracted. Pooled sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were synthesized using a bivariate mix-effects model. Meta-regression and subgroup analysis were conducted to test the source of heterogeneity., Results: In total, 11 studies with 14 AI/ML models were included in the final meta-analysis. The overall pooled AUC was 0.89 (95%CI 0.86-0.92). Additionally, the pooled Sen, Spe, PLR, NLR, and DOR were 0.78 (95%CI 0.71-0.84), 0.89 (95%CI 0.83-0.93), 6.99 (95%CI 4.38-11.14), 0.25 (95%CI 0.18-0.34) and 28.07 (95%CI 14.27-55.20), respectively. The results of meta-regression suggested that reference standard definitions might be the source of heterogeneity., Conclusion: AI/ML algorithms appear to be promising to help clinicians detect and identify patients at high risk of UTIs. However, further studies are demanded to evaluate the application value of AI/ML more thoroughly., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
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- 2024
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35. Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques.
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Baskar G, Nashath Omer S, Saravanan P, Rajeshkannan R, Saravanan V, Rajasimman M, and Shanmugam V
- Subjects
- Waste Disposal, Fluid methods, Recycling methods, Water Quality, Artificial Intelligence, Machine Learning, Wastewater chemistry
- Abstract
The two main things needed to fulfill the world's impending need for water in the face of the widespread water crisis are collecting water and recycling. To do this, the present study has placed a greater focus on water management strategies used in a variety of contexts areas. To distribute water effectively, save it, and satisfy water quality requirements for a variety of uses, it is imperative to apply intelligent water management mechanisms while keeping in mind the population density index. The present review unveiled the latest trends in water and wastewater recycling, utilizing several Artificial Intelligence (AI) and machine learning (ML) techniques for distribution, rainfall collection, and control of irrigation models. The data collected for these purposes are unique and comes in different forms. An efficient water management system could be developed with the use of AI, Deep Learning (DL), and the Internet of Things (IoT) structure. This study has investigated several water management methodologies using AI, DL and IoT with case studies and sample statistical assessment, to provide an efficient framework for water management., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)
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- 2024
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36. Machine learning/artificial intelligence in sports medicine: state of the art and future directions.
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Pareek A, Ro DH, Karlsson J, and Martin RK
- Subjects
- Humans, Deep Learning, Neural Networks, Computer, Algorithms, Natural Language Processing, Machine Learning, Sports Medicine methods, Artificial Intelligence
- Abstract
Machine learning (ML) is changing the way health care is practiced and recent applications of these novel statistical techniques have started to impact orthopaedic sports medicine. Machine learning enables the analysis of large volumes of data to establish complex relationships between "input" and "output" variables. These relationships may be more complex than could be established through traditional statistical analysis and can lead to the ability to predict the "output" with high levels of accuracy. Supervised learning is the most common ML approach for healthcare data and recent studies have developed algorithms to predict patient-specific outcome after surgical procedures such as hip arthroscopy and anterior cruciate ligament reconstruction. Deep learning is a higher-level ML approach that facilitates the processing and interpretation of complex datasets through artificial neural networks that are inspired by the way the human brain processes information. In orthopaedic sports medicine, deep learning has primarily been used for automatic image (computer vision) and text (natural language processing) interpretation. While applications in orthopaedic sports medicine have been increasing exponentially, one significant barrier to widespread adoption of ML remains clinician unfamiliarity with the associated methods and concepts. The goal of this review is to introduce these concepts, review current machine learning models in orthopaedic sport medicine, and discuss future opportunities for innovation within the specialty., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)
- Published
- 2024
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37. Trusting AI made decisions in healthcare by making them explainable.
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Žlahtič B, Završnik J, Kokol P, Blažun Vošner H, Sobotkiewicz N, Antolinc Schaubach B, and Kirbiš S
- Subjects
- Humans, Telemedicine, Trust, Decision Making, Artificial Intelligence, Machine Learning, Delivery of Health Care, Algorithms
- Abstract
Objectives: In solving the trust issues surrounding machine learning algorithms whose reasoning cannot be understood, advancements can be made toward the integration of machine learning algorithms into mHealth applications. The aim of this paper is to provide a transparency layer to black-box machine learning algorithms and empower mHealth applications to maximize their efficiency., Methods: Using a machine learning testing framework, we present the process of knowledge transfer between a white-box model and a black-box model and the evaluation process to validate the success of the knowledge transfer., Results: The presentation layer of the final output of the base white-box model and the knowledge-infused white-box model shows clear differences in reasoning. The correlation between the base black-box model and the new knowledge-infused model is very high, indicating that the knowledge transfer was successful., Conclusion: There is a clear need for transparency in digital health and health in general. Adding solutions to the toolbox of explainable artificial intelligence is one way to gradually decrease the obscurity of black-box models., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
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- 2024
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38. A requirements engineering perspective to AI-based systems development: a vision paper
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Xavier Franch, Andreas Jedlitschka, Silverio Martínez-Fernández, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació, and Universitat Politècnica de Catalunya. inSSIDE - integrated Software, Services, Information and Data Engineering
- Subjects
Artificial intelligence ,Vision paper ,AI-based system ,AI ,Intel·ligència artificial ,Informàtica::Enginyeria del software [Àrees temàtiques de la UPC] ,Machine learning ,Enginyeria de requisits ,Requirements engineering ,RE ,ML - Abstract
Context and motivation: AI-based systems (i.e., systems integrating some AI model or component) are becoming pervasive in society. A number of characteristics of AI-based systems challenge classical requirements engineering (RE) and raise questions yet to be answered. Question: This vision paper inquires the role that RE should play in the development of AI-based systems with a focus on three areas: roles involved, requirements’ scope and non-functional requirements. Principal Ideas: The paper builds upon the vision that RE shall become the cornerstone in AI-based system development and proposes some initial ideas and roadmap for these three areas. Contribution: Our vision is a step towards clarifying the role of RE in the context of AI-based systems development. The different research lines outlined in the paper call for further research in this area. This paper is part of the project TED2021-130923B-I00, funded by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenera-tionEU”/PRTR.
- Published
- 2023
39. The Social Life of Small Urban Spaces 2.0 : Three Experiments in Computational Urban Studies
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Argota Sánchez-Vaquerizo, Javier, Cardoso Llach, Daniel, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, and Lee, Ji-Hyun, editor
- Published
- 2019
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40. Discussion paper: Social accountability for students in a machine learning era
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Williams, LZJ and Grainger, R
- Published
- 2020
41. LLM potentiality and awareness: a position paper from the perspective of trustworthy and responsible AI modeling.
- Author
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Sarker, Iqbal H.
- Subjects
LANGUAGE models ,TRUST ,ARTIFICIAL intelligence ,RISK perception ,AWARENESS - Abstract
Large language models (LLMs) are an exciting breakthrough in the rapidly growing field of artificial intelligence (AI), offering unparalleled potential in a variety of application domains such as finance, business, healthcare, cybersecurity, and so on. However, concerns regarding their trustworthiness and ethical implications have become increasingly prominent as these models are considered black-box and continue to progress. This position paper explores the potentiality of LLM from diverse perspectives as well as the associated risk factors with awareness. Towards this, we highlight not only the technical challenges but also the ethical implications and societal impacts associated with LLM deployment emphasizing fairness, transparency, explainability, trust and accountability. We conclude this paper by summarizing potential research scopes with direction. Overall, the purpose of this position paper is to contribute to the ongoing discussion of LLM potentiality and awareness from the perspective of trustworthiness and responsibility in AI. [ABSTRACT FROM AUTHOR]
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- 2024
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42. Interpretation of cluster structures in pain-related phenotype data using explainable artificial intelligence (XAI).
- Author
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Lötsch J and Malkusch S
- Subjects
- Algorithms, Cluster Analysis, Humans, Pain, Phenotype, Artificial Intelligence, Machine Learning
- Abstract
Background: In pain research and clinics, it is common practice to subgroup subjects according to shared pain characteristics. This is often achieved by computer-aided clustering. In response to a recent EU recommendation that computer-aided decision making should be transparent, we propose an approach that uses machine learning to provide (1) an understandable interpretation of a cluster structure to (2) enable a transparent decision process about why a person concerned is placed in a particular cluster., Methods: Comprehensibility was achieved by transforming the interpretation problem into a classification problem: A sub-symbolic algorithm was used to estimate the importance of each pain measure for cluster assignment, followed by an item categorization technique to select the relevant variables. Subsequently, a symbolic algorithm as explainable artificial intelligence (XAI) provided understandable rules of cluster assignment. The approach was tested using 100-fold cross-validation., Results: The importance of the variables of the data set (6 pain-related characteristics of 82 healthy subjects) changed with the clustering scenarios. The highest median accuracy was achieved by sub-symbolic classifiers. A generalized post-hoc interpretation of clustering strategies of the model led to a loss of median accuracy. XAI models were able to interpret the cluster structure almost as correctly, but with a slight loss of accuracy., Conclusions: Assessing the variables importance in clustering is important for understanding any cluster structure. XAI models are able to provide a human-understandable interpretation of the cluster structure. Model selection must be adapted individually to the clustering problem. The advantage of comprehensibility comes at an expense of accuracy., (© 2020 The Authors. European Journal of Pain published by John Wiley & Sons Ltd on behalf of European Pain Federation - EFIC®.)
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- 2021
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43. Special issue on intelligent systems: ISMIS 2022 selected papers.
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Ceci, Michelangelo, Flesca, Sergio, Manco, Giuseppe, and Masciari, Elio
- Subjects
MACHINE learning ,ARTIFICIAL intelligence ,DECISION support systems ,KNOWLEDGE representation (Information theory) ,COMPUTER vision ,DEEP learning - Abstract
This document is a special issue of the Journal of Intelligent Information Systems, focusing on the selected papers from the International Symposium on Methodologies for Intelligent Systems (ISMIS 2022). The symposium, held in Cosenza, Italy, showcased research on various topics related to artificial intelligence, including decision support, knowledge representation, machine learning, computer vision, and more. The special issue includes eleven papers that have undergone rigorous peer-reviewing and cover a wide range of research topics, such as deep learning, anomaly detection, malware detection, sentiment classification, and healthcare professionals' burnout. The authors express their gratitude to the contributors and reviewers for their valuable contributions. [Extracted from the article]
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- 2024
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44. Venous thromboembolism in the era of machine learning and artificial intelligence in medicine.
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Reyes Gil M, Pantanowitz J, and Rashidi HH
- Subjects
- Humans, Venous Thromboembolism therapy, Venous Thromboembolism diagnosis, Machine Learning, Artificial Intelligence
- Abstract
In this review, we embark on a comprehensive exploration of venous thromboembolism (VTE) in the context of medical history and its current practice within medicine. We delve into the landscape of artificial intelligence (AI), exploring its present utility and envisioning its transformative roles within VTE management, from prevention to screening and beyond. Central to our discourse is a forward-looking perspective on the integration of AI within VTE in medicine, advocating for rigorous study design, robust validation processes, and meticulous statistical analysis to gauge the efficacy of AI applications. We further illuminate the potential of large language models and generative AI in revolutionizing VTE care, while acknowledging their inherent limitations and proposing innovative solutions to overcome challenges related to data availability and integrity, including the strategic use of synthetic data. The critical importance of navigating ethical, legal, and privacy concerns associated with AI is underscored, alongside the imperative for comprehensive governance and policy frameworks to regulate its deployment in VTE treatment. We conclude on a note of cautious optimism, where we highlight the significance of proactively addressing the myriad challenges that accompany the advent of AI in healthcare. Through diligent design, stringent validation, extensive education, and prudent regulation, we can harness AI's potential to significantly enhance our understanding and management of VTE. As we stand on the cusp of a new era, our commitment to these principles will be instrumental in ensuring that the promise of AI is fully realized within the realm of VTE care., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Hooman H. Rashidi is the co-inventor of the Synthetic Tabular Neural Generator (STNG) platform, (STNG is the Intellectual Property of the Cleveland Clinic). Hooman H. Rashidi is also co-inventor of MiLO and co-founder of the University of California start-up MILO-ML, Inc. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
- Published
- 2024
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45. A Visionary Way to Novel Process Optimizations : The Marriage of the Process Domain and Deep Neuronal Networks
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Grum, Marcus, Gronau, Norbert, van der Aalst, Wil M. P., Series Editor, Mylopoulos, John, Series Editor, Rosemann, Michael, Series Editor, Shaw, Michael J., Series Editor, Szyperski, Clemens, Series Editor, and Shishkov, Boris, editor
- Published
- 2018
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46. BRAIN-INSPIRED AI CODE LIBRARY PASSES MAJOR MILESTONE, NEW PAPER OFFERS PERSPECTIVE ON FUTURE OF FIELD
- Subjects
Artificial intelligence ,Neurosciences ,Machine learning ,Neural networks ,Neural network ,Artificial intelligence ,News, opinion and commentary ,University of California, Santa Cruz - Abstract
SANTA CRUZ, Calif. -- The following information was released by the University of California - Santa Cruz: By Emily Cerf Spiking neural networks, a form of low-power, brain-inspired deep learning, [...]
- Published
- 2023
47. Call for Papers—INFORMS Journal on Computing: Special Issue on Responsible AI and Data Science for Social Good.
- Subjects
- *
DATA science , *SOFTWARE architecture , *ARTIFICIAL intelligence , *MACHINE learning , *ARTIFICIAL neural networks , *SWARM intelligence - Published
- 2023
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48. A roadmap to artificial intelligence (AI): Methods for designing and building AI ready data to promote fairness.
- Author
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Kidwai-Khan F, Wang R, Skanderson M, Brandt CA, Fodeh S, and Womack JA
- Subjects
- Humans, Fractures, Bone, Female, Electronic Health Records, Artificial Intelligence, Natural Language Processing, Accidental Falls prevention & control, Algorithms, Machine Learning
- Abstract
Objectives: We evaluated methods for preparing electronic health record data to reduce bias before applying artificial intelligence (AI)., Methods: We created methods for transforming raw data into a data framework for applying machine learning and natural language processing techniques for predicting falls and fractures. Strategies such as inclusion and reporting for multiple races, mixed data sources such as outpatient, inpatient, structured codes, and unstructured notes, and addressing missingness were applied to raw data to promote a reduction in bias. The raw data was carefully curated using validated definitions to create data variables such as age, race, gender, and healthcare utilization. For the formation of these variables, clinical, statistical, and data expertise were used. The research team included a variety of experts with diverse professional and demographic backgrounds to include diverse perspectives., Results: For the prediction of falls, information extracted from radiology reports was converted to a matrix for applying machine learning. The processing of the data resulted in an input of 5,377,673 reports to the machine learning algorithm, out of which 45,304 were flagged as positive and 5,332,369 as negative for falls. Processed data resulted in lower missingness and a better representation of race and diagnosis codes. For fractures, specialized algorithms extracted snippets of text around keywork "femoral" from dual x-ray absorptiometry (DXA) scans to identify femoral neck T-scores that are important for predicting fracture risk. The natural language processing algorithms yielded 98% accuracy and 2% error rate The methods to prepare data for input to artificial intelligence processes are reproducible and can be applied to other studies., Conclusion: The life cycle of data from raw to analytic form includes data governance, cleaning, management, and analysis. When applying artificial intelligence methods, input data must be prepared optimally to reduce algorithmic bias, as biased output is harmful. Building AI-ready data frameworks that improve efficiency can contribute to transparency and reproducibility. The roadmap for the application of AI involves applying specialized techniques to input data, some of which are suggested here. This study highlights data curation aspects to be considered when preparing data for the application of artificial intelligence to reduce bias., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)
- Published
- 2024
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49. Artificial intelligence and Machine Learning approaches in sports: Concepts, applications, challenges, and future perspectives.
- Author
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Reis FJJ, Alaiti RK, Vallio CS, and Hespanhol L
- Subjects
- Humans, Athletic Injuries, Athletic Performance, Machine Learning, Artificial Intelligence, Sports
- Abstract
Background: The development and application of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare have gained attention as a promising and powerful resource to change the landscape of healthcare. The potential of these technologies for injury prediction, performance analysis, personalized training, and treatment comes with challenges related to the complexity of sports dynamics and the multidimensional aspects of athletic performance., Objectives: We aimed to present the current state of AI and ML applications in sports science, specifically in the areas of injury prediction, performance enhancement, and rehabilitation. We also examine the challenges of incorporating AI and ML into sports and suggest directions for future research., Method: We conducted a comprehensive literature review, focusing on publications related to AI and ML applications in sports. This review encompassed studies on injury prediction, performance analysis, and personalized training, emphasizing the AI and ML models applied in sports., Results: The findings highlight significant advancements in injury prediction accuracy, performance analysis precision, and the customization of training programs through AI and ML. However, future studies need to address challenges such as ethical considerations, data quality, interpretability of ML models, and the integration of complex data., Conclusion: AI and ML may be useful for the prevention, detection, diagnosis, and treatment of health conditions. In this Masterclass paper, we introduce AI and ML concepts, outline recent breakthroughs in AI technologies and their applications, identify the challenges for further progress of AI systems, and discuss ethical issues, clinical and research opportunities, and future perspectives., Competing Interests: Declaration of competing interest The authors declare no competing interest., (Copyright © 2024 Associação Brasileira de Pesquisa e Pós-Graduação em Fisioterapia. Publicado por Elsevier España, S.L.U. All rights reserved.)
- Published
- 2024
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50. Don't Fear the Artificial Intelligence: A Systematic Review of Machine Learning for Prostate Cancer Detection in Pathology.
- Author
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Frewing A, Gibson AB, Robertson R, Urie PM, and Corte DD
- Subjects
- Humans, Male, Neoplasm Grading, Algorithms, Prostatic Neoplasms diagnosis, Prostatic Neoplasms pathology, Machine Learning, Artificial Intelligence
- Abstract
Context: Automated prostate cancer detection using machine learning technology has led to speculation that pathologists will soon be replaced by algorithms. This review covers the development of machine learning algorithms and their reported effectiveness specific to prostate cancer detection and Gleason grading., Objective: To examine current algorithms regarding their accuracy and classification abilities. We provide a general explanation of the technology and how it is being used in clinical practice. The challenges to the application of machine learning algorithms in clinical practice are also discussed., Data Sources: The literature for this review was identified and collected using a systematic search. Criteria were established prior to the sorting process to effectively direct the selection of studies. A 4-point system was implemented to rank the papers according to their relevancy. For papers accepted as relevant to our metrics, all cited and citing studies were also reviewed. Studies were then categorized based on whether they implemented binary or multi-class classification methods. Data were extracted from papers that contained accuracy, area under the curve (AUC), or κ values in the context of prostate cancer detection. The results were visually summarized to present accuracy trends between classification abilities., Conclusions: It is more difficult to achieve high accuracy metrics for multiclassification tasks than for binary tasks. The clinical implementation of an algorithm that can assign a Gleason grade to clinical whole slide images (WSIs) remains elusive. Machine learning technology is currently not able to replace pathologists but can serve as an important safeguard against misdiagnosis., (© 2024 College of American Pathologists.)
- Published
- 2024
- Full Text
- View/download PDF
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