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Artificial intelligence (AI) has shown tremendous growth over the last decade, with the more recent development of clinical applications in health care. The ability of AI to synthesize large amounts of complex data automatically allows health care providers to access previously unavailable metrics and thus enhance and personalize patient care. These innovations include AI-assisted diagnostic tools, prediction models for each treatment pathway, and various tools for workflow optimization. The extension of AI into sports medicine is still early, but numerous AI-driven algorithms, devices, and research initiatives have delved into predicting and preventing athlete injury, aiding in injury assessment, optimizing recovery plans, monitoring rehabilitation progress, and predicting return to play., Competing Interests: None declared., (Thieme. All rights reserved.)

The integration of artificial intelligence (AI) into intensive care medicine has made considerable progress in recent studies, particularly in the areas of predictive analytics, early detection of complications, and the development of decision support systems. The main challenges remain availability and quality of data, reduction of bias and the need for explainable results from algorithms and models. Methods to explain these systems are essential to increase trust, understanding, and ethical considerations among healthcare professionals and patients. Proper training of healthcare professionals in AI principles, terminology, ethical considerations, and practical application is crucial for the successful use of AI. Careful assessment of the impact of AI on patient autonomy and data protection is essential for its responsible use in intensive care medicine. A balance between ethical and practical considerations must be maintained to ensure patient-centered care while complying with data protection regulations. Synergistic collaboration between clinicians, AI engineers, and regulators is critical to realizing the full potential of AI in intensive care medicine and maximizing its positive impact on patient care. Future research and development efforts should focus on improving AI models for real-time predictions, increasing the accuracy and utility of AI-based closed-loop systems, and overcoming ethical, technical, and regulatory challenges, especially in generative AI systems., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

The relationship between ethics and artificial intelligence in medicine is a crucial and complex topic that falls within its broader context. Ethics in medical artificial intelligence (AI) involves ensuring that technologies are safe, fair, and respect patient privacy. This includes concerns about the accuracy of diagnoses provided by artificial intelligence, fairness in patient treatment, and protection of personal health data. Advances in artificial intelligence can significantly improve healthcare, from more accurate diagnoses to personalized treatments. However, it is essential that developments in medical artificial intelligence are carried out with strong ethical consideration, involving healthcare professionals, artificial intelligence experts, patients, and ethics specialists to guide and oversee their implementation. Finally, transparency in artificial intelligence algorithms and ongoing training for medical professionals are fundamental., (Copyright © 2024 Elsevier España, S.L.U. and Sociedad Española de Medicina Interna (SEMI). All rights reserved.)

Artificial intelligence (AI) broadly describes a branch of computer science focused on developing machines capable of performing tasks typically associated with human intelligence. Those who connect AI with the world of science fiction may meet its growing rise with hesitancy or outright skepticism. However, AI is becoming increasingly pervasive in our society, from algorithms helping to sift through airline fares to substituting words in emails and SMS text messages based on user choices. Data collection is ongoing and is being leveraged by software platforms to analyze patterns and make predictions across multiple industries. Health care is gradually becoming part of this technological transformation, as advancements in computational power and storage converge with the rapid expansion of digitized medical information. Given the growing and inevitable integration of AI into health care systems, it is our viewpoint that pediatricians urgently require training and orientation to the uses, promises, and pitfalls of AI in medicine. AI is unlikely to solve the full array of complex challenges confronting pediatricians today; however, if used responsibly, it holds great potential to improve many aspects of care for providers, children, and families. Our aim in this viewpoint is to provide clinicians with a targeted introduction to the field of AI in pediatrics, including key promises, pitfalls, and clinical applications, so they can play a more active role in shaping the future impact of AI in medicine., (©Hansa Bhargava, Carmela Salomon, Srinivasan Suresh, Anthony Chang, Rachel Kilian, Diana van Stijn, Albert Oriol, Daniel Low, Ashley Knebel, Sharief Taraman. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.02.2024.)

Scientific research is driven by allocation of funding to different research projects based in part on the predicted scientific impact of the work. Data-driven algorithms can inform decision-making of scarce funding resources by identifying likely high-impact studies using bibliometrics. Compared to standardized citation-based metrics alone, we utilize a machine learning pipeline that analyzes high-dimensional relationships among a range of bibliometric features to improve the accuracy of predicting high-impact research. Random forest classification models were trained using 28 bibliometric features calculated from a dataset of 1,485,958 publications in medicine to retrospectively predict whether a publication would become high-impact. For each random forest model, the balanced accuracy score was above 0.95 and the area under the receiver operating characteristic curve was above 0.99. The high performance of high impact research prediction using our proposed models show that machine learning technologies are promising algorithms that can support funding decision-making for medical research., (© 2024. The Author(s).)

Convolutional neural network (CNN) has promoted the development of diagnosis technology of medical images. However, the performance of CNN is limited by insufficient feature information and inaccurate attention weight. Previous works have improved the accuracy and speed of CNN but ignored the uncertainty of the prediction, that is to say, uncertainty of CNN has not received enough attention. Therefore, it is still a great challenge for extracting effective features and uncertainty quantification of medical deep learning models In order to solve the above problems, this paper proposes a novel convolutional neural network model named DM-CNN, which mainly contains the four proposed sub-modules : dynamic multi-scale feature fusion module (DMFF), hierarchical dynamic uncertainty quantifies attention (HDUQ-Attention) and multi-scale fusion pooling method (MF Pooling) and multi-objective loss (MO loss). DMFF select different convolution kernels according to the feature maps at different levels, extract different-scale feature information, and make the feature information of each layer have stronger representation ability for information fusion HDUQ-Attention includes a tuning block that adjust the attention weight according to the different information of each layer, and a Monte-Carlo (MC) dropout structure for quantifying uncertainty MF Pooling is a pooling method designed for multi-scale models, which can speed up the calculation and prevent overfitting while retaining the main important information Because the number of parameters in the backbone part of DM-CNN is different from other modules, MO loss is proposed, which has a fast optimization speed and good classification effect DM-CNN conducts experiments on publicly available datasets in four areas of medicine (Dermatology, Histopathology, Respirology, Ophthalmology), achieving state-of-the-art classification performance on all datasets. DM-CNN can not only maintain excellent performance, but also solve the problem of quantification of uncertainty, which is a very important task for the medical field. The code is available: https://github.com/QIANXIN22/DM-CNN., Competing Interests: Declaration of competing interest The authors declare that they have no conflicts of interest., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Objective: Applications of machine learning in healthcare are of high interest and have the potential to improve patient care. Yet, the real-world accuracy of these models in clinical practice and on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate methods that predict healthcare outcomes. We focused on the prediction of all-cause mortality as the community challenge question., Materials and Methods: Using a Model-to-Data framework, 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries, generated 25 accurate models all trained on a dataset of over 1.1 million patients and evaluated on patients prospectively collected over a 1-year observation of a large health system., Results: The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI, 0.942-0.951) and an area under the precision-recall curve of 0.487 (95% CI, 0.458-0.499) on a prospectively collected patient cohort., Discussion: Post hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data., Conclusion: This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Prediction of drug-target interactions (DTIs) is essential in medicine field, since it benefits the identification of molecular structures potentially interacting with drugs and facilitates the discovery and reposition of drugs. Recently, much attention has been attracted to network representation learning to learn rich information from heterogeneous data. Although network representation learning algorithms have achieved success in predicting DTI, several manually designed meta-graphs limit the capability of extracting complex semantic information. To address the problem, we introduce an adaptive meta-graph-based method, termed AMGDTI, for DTI prediction. In the proposed AMGDTI, the semantic information is automatically aggregated from a heterogeneous network by training an adaptive meta-graph, thereby achieving efficient information integration without requiring domain knowledge. The effectiveness of the proposed AMGDTI is verified on two benchmark datasets. Experimental results demonstrate that the AMGDTI method overall outperforms eight state-of-the-art methods in predicting DTI and achieves the accurate identification of novel DTIs. It is also verified that the adaptive meta-graph exhibits flexibility and effectively captures complex fine-grained semantic information, enabling the learning of intricate heterogeneous network topology and the inference of potential drug-target relationship., (© The Author(s) 2023. Published by Oxford University Press.)

Introduction: The use of artificial intelligence (AI), particularly machine learning and predictive analytics, has shown great promise in health care. Despite its strong potential, there has been limited use in health care settings. In this systematic review, we aim to determine the main barriers to successful implementation of AI in healthcare and discuss potential ways to overcome these challenges., Methods: We conducted a literature search in PubMed (1/1/2001-1/1/2023). The search was restricted to publications in the English language, and human study subjects. We excluded articles that did not discuss AI, machine learning, predictive analytics, and barriers to the use of these techniques in health care. Using grounded theory methodology, we abstracted concepts to identify major barriers to AI use in medicine., Results: We identified a total of 2,382 articles. After reviewing the 306 included papers, we developed 19 major themes, which we categorized into three levels: the Technical/Algorithm, Stakeholder, and Social levels (TASS). These themes included: Lack of Explainability, Need for Validation Protocols, Need for Standards for Interoperability, Need for Reporting Guidelines, Need for Standardization of Performance Metrics, Lack of Plan for Updating Algorithm, Job Loss, Skills Loss, Workflow Challenges, Loss of Patient Autonomy and Consent, Disturbing the Patient-Clinician Relationship, Lack of Trust in AI, Logistical Challenges, Lack of strategic plan, Lack of Cost-effectiveness Analysis and Proof of Efficacy, Privacy, Liability, Bias and Social Justice, and Education., Conclusion: We identified 19 major barriers to the use of AI in healthcare and categorized them into three levels: the Technical/Algorithm, Stakeholder, and Social levels (TASS). Future studies should expand on barriers in pediatric care and focus on developing clearly defined protocols to overcome these barriers., 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 © 2023 Elsevier Inc. All rights reserved.)

Artificial intelligence (AI) and its machine learning (ML) algorithms are offering new promise for personalized biomedicine and more cost-effective healthcare with impressive technical capability to mimic human cognitive capabilities. However, widespread application of this promising technology has been limited in the medical domain and expectations have been tampered by ethical challenges and concerns regarding patient privacy, legal responsibility, trustworthiness, and fairness. To balance technical innovation with ethical applications of AI/ML, developers must demonstrate the AI functions as intended and adopt strategies to minimize the risks for failure or bias. This review describes the new ethical challenges created by AI/ML for clinical care and identifies specific considerations for its practice in medicine. We provide an overview of regulatory and legal issues applicable in Europe and the United States, a description of technical aspects to consider, and present recommendations for trustworthy AI/ML that promote transparency, minimize risks of bias or error, and protect the patient well-being.

The Sine Cosine Algorithm (SCA) is an outstanding optimizer that is appreciably used to dissolve complicated real-world problems. Nevertheless, this algorithm lacks sufficient population diversification and a sufficient balance between exploration and exploitation. So, effective techniques are required to tackle the SCA's fundamental shortcomings. Accordingly, the present paper suggests an improved version of SCA called Hierarchical Multi-Leadership SCA (HMLSCA) which uses an effective hierarchical multi-leadership search mechanism to lead the search process on multiple paths. The efficiency of the HMLSCA has been appraised and compared with a set of famous metaheuristic algorithms to dissolve the classical eighteen benchmark functions and thirty CEC 2017 test suites. The results demonstrate that the HMLSCA outperforms all compared algorithms and that the proposed algorithm provided a promising efficiency. Moreover, the HMLSCA was applied to handle the medicine data classification by optimizing the support vector machine's (SVM) parameters and feature weighting in eight datasets. The experiential outcomes verify the productivity of the HMLSCA with the highest classification accuracy and a gain scoring 1.00 Friedman mean rank versus the other evaluated metaheuristic algorithms. Furthermore, the proposed algorithm was used to diagnose COVID-19, in which it attained the topmost accuracy of 98% in diagnosing the infection on the COVID-19 dataset, which proves the performance of the proposed search strategy., 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 © 2023 Elsevier Ltd. All rights reserved.)

The question of whether the time has come to hang up the stethoscope is bound up in the promises of artificial intelligence (AI), promises that have so far proven difficult to deliver, perhaps because of the mismatch between the technical capability of AI and its use in real-world clinical settings. This perspective argues that it is time to move away from discussing the generalised promise of disembodied AI and focus on specifics. We need to focus on how the computational method underlying AI, i.e. machine learning (ML), is embedded into tools, how those tools contribute to clinical tasks and decisions and to what extent they can be relied on. Accordingly, we pose four questions that must be asked to make the discussion real and to understand how ML tools contribute to health care: (1) What does the ML algorithm do? (2) How is output of the ML algorithm used in clinical tools? (3) What does the ML tool contribute to clinical tasks or decisions? (4) Can clinicians act or rely on the ML tool? Two exemplar ML tools are examined to show how these questions can be used to better understand the role of ML in supporting clinical tasks and decisions. Ultimately, ML is just a fancy method of automation. We show that it is useful in automating specific and narrowly defined clinical tasks but likely incapable of automating the full gamut of decisions and tasks performed by clinicians., (© 2023 The Authors. Internal Medicine Journal published by John Wiley & Sons Australia, Ltd on behalf of Royal Australasian College of Physicians.)

The tumor immune microenvironment (TIME) of colon cancer (CC) has been associated with extensive immune cell infiltration (IMI). Increasing evidence demonstrated that plasma cells (PC) have an extremely important role in advance of antitumor immunity. Nonetheless, there is a lack of comprehensive analyses of PC infiltration in clinical prognosis and immunotherapy in CC. This study systematically addressed the gene expression model and clinical information of CC patients. Clinical samples were obtained from the TCGA (The Cancer Genome Atlas) databases. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), GSVA, and the MAlignant Tumors using Expression data (ESTIMATE) algorithm were employed to research the potential mechanism and pathways. Immunophenoscore (IPS) was obtained to evaluate the immunotherapeutic significance of risk score. Half maximal inhibitory concentration (IC50) of chemotherapeutic medicine was predicted by employing the pRRophetic algorithm. A total of 513 CC samples (including 472 tumor samples and 41 normal samples) were collected from the TCGA-GDC database. Significant black modules and 313 candidate genes were considered PC-related genes by accessing WGCNA. Five pivotal genes were established through multiple analyses, which revealed excellent prognostic. The underlying correlation between risk score with tumor mutation burden (TMB) was further explored. In addition, the risk score was obviously correlated with various tumor immune microenvironment (TIME). Also, risk CC samples showed various signaling pathways activity and different pivotal sensitivities to administering chemotherapy. Finally, the biological roles of the CD177 gene were uncovered in CC., (© 2023. The Author(s).)

Brain-computer interfaces (BCIs) have become one of the cutting-edge technologies in the world, and have been mainly applicated in medicine. In this article, we sorted out the development history and important scenarios of BCIs in medical application, analyzed the research progress, technology development, clinical transformation and product market through qualitative and quantitative analysis, and looked forward to the future trends. The results showed that the research hotspots included the processing and interpretation of electroencephalogram (EEG) signals, the development and application of machine learning algorithms, and the detection and treatment of neurological diseases. The technological key points included hardware development such as new electrodes, software development such as algorithms for EEG signal processing, and various medical applications such as rehabilitation and training in stroke patients. Currently, several invasive and non-invasive BCIs are in research. The R&D level of BCIs in China and the United State is leading the world, and have approved a number of non-invasive BCIs. In the future, BCIs will be applied to a wider range of medical fields. Related products will develop shift from a single mode to a combined mode. EEG signal acquisition devices will be miniaturized and wireless. The information flow and interaction between brain and machine will give birth to brain-machine fusion intelligence. Last but not least, the safety and ethical issues of BCIs will be taken seriously, and the relevant regulations and standards will be further improved.

Although deep-learning algorithms in dermatology have shown promise in diagnosing skin cancers, less is known about potential applications for the diagnosis of infectious diseases. In a recent publication in Nature Medicine, Thieme et al. develop a deep-learning algorithm to classify skin lesions from Mpox virus (MPXV) infections. 1 ., Competing Interests: Declaration of interests A.S.A. is a web and associate editor at JAMA Dermatology., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Complexity of medical treatments can vary from prescribing medicine for a specific ailment to managing a complex set of simultaneous medical issues. In the latter case, doctors are assisted by clinical guidelines which outline standard medical procedures, tests, treatments, etc. To facilitate the use of such guidelines, they can be digitized as processes and adopted in complex process engines offering additional help to health providers such as decision support while monitoring active treatments so as to detect flaws in treatment procedures and suggest possible reactions on them. For example, a patient may present symptoms of multiple diseases simultaneously (requiring multiple clinical guidelines to be followed), while also being allergic to some often-used drugs (requiring additional constraints to be respected). This can easily lead to treating a patient based on a set of process specifications which are not fully compatible with each other. While a scenario like that commonly occurs in practice, research in that direction has thus far given little consideration to how to specify multiple clinical guidelines and how to automatically combine their specifications in the context of the monitoring task. In our previous work (Alman et al., 20222), we presented a conceptual framework for handling the above cases in the context of monitoring. In this paper, we present the algorithms necessary for implementing key components of this conceptual framework. More specifically, we provide formal languages for representing clinical guideline specifications and formalize a solution for monitoring the interplay of such specifications expressed as a combination of (data-aware) Petri nets and temporal logic rules. The proposed solution seamlessly handles combination of the input process specifications and provides both early conflict detection and decision support during process execution. We also discuss a proof-of-concept implementation of our approach and present the results of extensive scalability experiments., (Copyright © 2023. Published by Elsevier B.V.)

Background: With the rapid development of the medical industry and the gradual increase in people's awareness of their health, the use of the Internet for medical question and answer, to obtain more accurate medical answers. It is necessary to first calculate the similarity of the questions asked by users, which further matches professional medical answers. Improving the efficiency of online medical question and answer sessions will not only reduce the burden on doctors, but also enhance the patient's experience of online medical diagnosis., Method: This paper focuses on building a bidirectional gated recurrent unit(BiGRU) deep learning model based on Siamese network for medical interrogative similarity matching, using Word2Vec word embedding tool for word vector processing of ethnic-medical corpus, and introducing an attention mechanism and convolutional neural network. Bidirectional gated recurrent unit extracts contextual semantic information and long-distance dependency features of interrogative sentences; Similar ethnic medicine interrogatives vary in length and structure, and the key information in the interrogative is crucial to similarity identification. By introducing an attention mechanism higher weight can be given to the keywords in the question, further improving the recognition of similar words in the question. Convolutional neural network takes into account the local information of interrogative sentences and can capture local position invariance, allowing feature extraction for words of different granularity through convolutional operations; By comparing the Euclidean distance, cosine distance and Manhattan distance to calculate the spatial distance of medical interrogatives, the Manhattan distance produced the best similarity result., Result: Based on the ethnic medical question dataset constructed in this paper, the accuracy and F1-score reached 97.24% and 97.98%, which is a significant improvement compared to several other models., Conclusion: By comparing with other models, the model proposed in this paper has better performance and achieve accurate matching of similar semantic question data of ethnic medicine., (© 2023. The Author(s).)

Background: Additive manufacturing enables to print patient-specific Foot Orthotics (FOs). In FOs featuring lattice structures, the variation of the cell's dimensions provides a locally variable stiffness to meet the therapeutic needs of each patient. In an optimization problem, however, using explicit Finite Element (FE) simulation of lattice FOs with converged 3D elements is computationally prohibitive. This paper presents a framework to efficiently optimize the cell's dimensions of a honeycomb lattice FO for flat foot condition., Methods: We built a surrogate based on shell elements whose mechanical properties were computed by the numerical homogenization technique. The model was submitted to a static pressure distribution of a flat foot and it predicted the displacement field for a given set of geometrical parameters of the honeycomb FO. This FE simulation was considered as a black-box and a derivative-free optimization solver was employed. The cost function was defined based on the difference between the predicted displacement by the model against a therapeutic target displacement., Results: Using the homogenized model as a surrogate significantly accelerated the stiffness optimization of the lattice FO. The homogenized model could predict the displacement field 78 times faster than the explicit model. When 2000 evaluations were required in an optimization problem, the computational time was reduced from 34 days to 10 hours using the homogenized model rather than explicit model. Moreover, in the homogenized model, there was no need to re-create and re-mesh the insole's geometry in each iteration of the optimization. It was only required to update the effective properties., Conclusion: The presented homogenized model can be used as a surrogate within an optimization framework to customize cell's dimensions of honeycomb lattice FO in a computationally efficient manner., 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 © 2022 Elsevier Ltd. All rights reserved.)

With the advent of artificial intelligence (AI), machines are increasingly being used to complete complicated tasks, yielding remarkable results. Machine learning (ML) is the most relevant subset of AI in medicine, which will soon become an integral part of our everyday practice. Therefore, physicians should acquaint themselves with ML and AI, and their role as an enabler rather than a competitor. Herein, we introduce basic concepts and terms used in AI and ML, and aim to demystify commonly used AI/ML algorithms such as learning methods including neural networks/deep learning, decision tree and application domain in computer vision and natural language processing through specific examples. We discuss how machines are already being used to augment the physician's decision-making process, and postulate the potential impact of ML on medical practice and medical research based on its current capabilities and known limitations. Moreover, we discuss the feasibility of full machine autonomy in medicine., Competing Interests: None

Objectives: Machine learning (ML) has the potential to facilitate "continual learning" in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such "adaptive" ML systems in medicine that have, thus far, been neglected in the literature., Target Audience: The target audiences for this tutorial are the developers of ML AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians., Scope: Discussions of adaptive ML systems to date have overlooked the distinction between 2 sorts of variance that such systems may exhibit-diachronic evolution (change over time) and synchronic variation (difference between cotemporaneous instantiations of the algorithm at different sites)-and underestimated the significance of the latter. We highlight the challenges that diachronic evolution and synchronic variation present for the quality of patient care, informed consent, and equity, and discuss the complex ethical trade-offs involved in the design of such systems., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Internal medicine departments must adapt their structures and methods of operation to accommodate changing healthcare systems. The present paper discusses some challenges departments of medicine face as healthcare providers and consumers continue to change. A co-pilot model is described in this article for augmenting physicians rather than replacing them. The paper presents the co-pilot models to improve diagnoses, treatments, and monitoring. Personalized variability patterns based on the constrained-disorder principle (CDP) are described to assess chronic therapies' effectiveness in improving patient outcomes. Based on CDP-based enhanced digital twins, this paper presents personalized treatments and follow-ups that improve diagnosis accuracy and therapy outcomes. While maintaining their professional values, departments of internal medicine must respond proactively to the needs of patients and healthcare systems. To meet the needs of patients and healthcare systems, they must strive for medical professionalism and adapt to the dynamic environment., Competing Interests: Declaration of Conflicting InterestsThe author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. DisclosureYI is the founder of Oberon Sciences

Objective: The aim: To improve primary prophylactic measures associated with the development and progression of recurrent bronchial obstruction syndrome in young children, who had suffered respiratory disorders in neonatal period., Patients and Methods: Materials and methods: Algorithm of primary prophylactic measures implied adequate balanced nutrition, sanation of living conditions, restriction of contact with infectious agents, sanation of chronic foci of infection, systematic training and general fitness. The investigation included 160 young children (1 day - 3 years of age). The basic group (n=80) involved children, who had experienced respiratory disorders in neonatal period and received appropriate respiratory therapy (artificial ventilation and / or spontaneous breathing with continuous positive airway pressure and supply of free oxygen), control group - children, who did not have respiratory disorders and respiratory therapy (n=80)., Results: Results: Conducted investigation throughout 12-month monitoring enabled to record the development of recurrent bronchial obstruction syndrome in 43 children (respectively, 30 - 37.50% patients of the basic group versus 13 - 16.25% of control group; p 0.05), could not be obtained., Conclusion: Conclusions: Comparative analysis within groups did not show a reliable difference in the development of recurrent bronchial obstruction syndrome in children (р>0.05), which can be explained by partial following of doctor's recommendations. There is the need in further study of the issue involving more patients for a longer period of monitoring.

The rise of machine learning in healthcare has significant implications for paediatrics. Long-term conditions with significant disease heterogeneity comprise large portions of the routine work performed by paediatricians. Improving outcomes through discovery of disease and treatment prediction models, alongside novel subgroup clustering of patients, are some of the areas in which machine learning holds significant promise. While artificial intelligence has percolated into routine use in our day to day lives through advertising algorithms, song or movie selections and sifting of spam emails, the ability of machine learning to utilise highly complex and dimensional data has not yet reached its full potential in healthcare. In this review article, we discuss some of the foundations of machine learning, including some of the basic algorithms. We emphasise the importance of correct utilisation of machine learning, including adequate data preparation and external validation. Using nutrition in preterm infants and paediatric inflammatory bowel disease as examples, we discuss the evidence and potential utility of machine learning in paediatrics. Finally, we review some of the future applications, alongside challenges and ethical considerations related to application of artificial intelligence. IMPACT: Machine learning is a widely used term; however, understanding of the process and application to healthcare is lacking. This article uses clinical examples to explore complex machine learning terms and algorithms. We discuss limitations and potential future applications within paediatrics and neonatal medicine., (© 2022. The Author(s).)

Biosignal-based technology has been increasingly available in our daily life, being a critical information source. Wearable biosensors have been widely applied in, among others, biometrics, sports, health care, rehabilitation assistance, and edutainment. Continuous data collection from biodevices provides a valuable volume of information, which needs to be curated and prepared before serving machine learning applications. One of the universal preparation steps is data segmentation and labelling/annotation. This work proposes a practical and manageable way to automatically segment and label single-channel or multimodal biosignal data using a self-similarity matrix (SSM) computed with signals' feature-based representation. Applied to public biosignal datasets and a benchmark for change point detection, the proposed approach delivered lucid visual support in interpreting the biosignals with the SSM while performing accurate automatic segmentation of biosignals with the help of the novelty function and associating the segments grounded on their similarity measures with the similarity profiles. The proposed method performed superior to other algorithms in most cases of a series of automatic biosignal segmentation tasks; of equal appeal is that it provides an intuitive visualization for information retrieval of multimodal biosignals.

Artificial intelligence (AI) promises a significant transformation of health care in all medical areas, which could represent "Gutenberg moment" for medicine. The future of medical specialties came largely from human interaction and creativity, forcing physicians to evolve and use AI as a tool in patient care. AI will offer patients safety, autonomy, and access to timely medical care in hard-to-reach areas while reducing administrative burden, screen time, and professional burnout for physicians. AI will also make it possible to reduce the frequency of medical errors and improve diagnostic accuracy through the integration, analysis, and interpretation of information by algorithms and software. The safety of repetitive activities will free up time for health personnel and will enhance the doctor-patient relationship, return to personalized attention and interaction with the patient, through accompaniment, communication, empathy, and trust during illness, activities that will never be replaced by AI. It is still necessary to standardize research in the area, which allows improving the quality of scientific evidence knowing its advantages and risks, accelerating its implementation in current medical practice.

Background: Subject-wise modeling using machine learning is useful in many applications requiring low error and complexity, such as wearable medical devices. However, regression accuracy depends highly on the data available to train the model and the model's generalization ability. Adversely, the prediction error may increase severely if unknown data patterns test the model; such a model is known to be overfitted. In medicine-related applications, such as Non-Invasive Blood Pressure (NIBP) estimation, the high error renders the estimation model useless and dangerous., Methods: This paper presents a novel algorithm to handle overfitting by editing the training data to achieve generalization for subject-wise models. The pooling and patching (PaP) algorithms use a relatively short record segment of a subject as a Key-Segment (KS) to search through a larger dataset for similar subjects. Then samples taken from the matched subjects' pool records are used to patch the original subject's KS. Due to the significance of systolic blood pressure (SBP) and the complexity of its variability, non-invasive estimation of SBP from electrocardiography (ECG) and photoplethysmography (PPG) is introduced as an application to assess the algorithm. The study was performed on 2051 subjects with a wide range of age, height, weight, length, and health status. The subjects' records were taken from a large public dataset, VitalDB, which is acquired from subjects undergoing different surgeries. Finally, all the results are obtained without using other model generalization techniques., Results: The generalization effect of the proposed algorithm, PaP, significantly outperformed cross-validation, which is widely used in regression model generalization. Moreover, the testing results show that a KS of 200 to 2000 samples is sufficient for providing high accuracy for much longer testing data of about 12000 to 24000 samples long, which is less than %10 of the record length on average. Furthermore, compared to other works based on the same dataset, PaP provides a significantly lower mean error of -0.75 ± 5.51 mmHg, with a small training data portion of 15% over 2051 subjects., 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 © 2022 Elsevier Ltd. All rights reserved.)

With the continued integration of technology in medicine, large amounts of patient data are often vulnerable to cyber-attacks. Medical data must be secured, however traditional cryptographic algorithms are inapplicable to medical images due to factors such as bulk data capacity, strong correlation among adjacent pixels, and high redundancy. To address the need for new medical image encryption algorithms, a novel approach based on the central dogma of molecular biology is proposed. The resulting algorithm has a linear runtime complexity, and is resistant to brute force, differential and statistical attacks. The algorithm advances the state-of-the-art in DNA-based image encryption and surpasses recent approaches in medical image encryption in its defence against cyber-attacks. Clinical Relevance- Secure data transmission and storage is critical for patient privacy. This algorithm increases the security of patient imaging when compared to image encryption algorithms in literature.

Magnetic resonance imaging (MRI) is a powerful imaging modality that revolutionizes medicine and biology. The imaging speed of high -dimensional MRI is often limited, which constrains its practical utility. Recently, low-rank tensor models have been exploited to enable fast MR imaging with sparse sampling. Most existing methods use some pre-defined sampling design, and active sensing has not been explored for low-rank tensor imaging. In this paper, we introduce an active low-rank tensor model for fast MR imaging. We propose an active sampling method based on a Query-by-Committee model, making use of the benefits of low-rank tensor structure. Numerical experiments on a 3-D MRI data set with Cartesian sampling designs demonstrate the effectiveness of the proposed method.

Artificial Intelligence (AI) has made major progress in recent years in many fields. With regard of medicine however, the utilization of AI raises numerous ethical questions, especially since newer and much more accurate algorithms function as black boxes. A trade-off must then be made between having algorithms being very accurate and effective, and algorithms that are explainable but less proficient. In this paper we address the ethical implications of utilizing black box algorithms in medicine.

In 2022, the Medical Informatics Europe conference created a special topic called "Challenges of trustable AI and added-value on health" which was centered around the theme of eXplainable Artificial Intelligence. Unfortunately, two opposite views remain for biomedical applications of machine learning: accepting to use reliable but opaque models, vs. enforce models to be explainable. In this contribution we discuss these two opposite approaches and illustrate with examples the differences between them.

Background: The continued advancement of digitalization increasingly allows deployment of artificial intelligence (AI) algorithms, leveraging profound effects on society and medicine., Objective: This article aims to provide an overview of current developments and futures perspectives of AI in otorhinolaryngology., Materials and Methods: Scientific studies and expert analyses were evaluated and discussed., Results: AI can increase the value of current diagnostic tools in otorhinolaryngology and enhance surgical precision in head and neck surgery., Conclusion: AI has the potential to further improve diagnostic and therapeutic procedures in otorhinolaryngology. This technology, however, is associated with challenges, for example in the domain of privacy and data security., (© 2021. The Author(s).)

Artificial intelligence (AI) algorithms are being applied across a large spectrum of everyday life activities. The implementation of AI algorithms in clinical practice has been met with some skepticism and concern, mainly because of the uneasiness that stems, in part, from a lack of understanding of how AI operates, together with the role of physicians and patients in the decision-making process; uncertainties regarding the reliability of the data and the outcomes; as well as concerns regarding the transparency, accountability, liability, handling of personal data, and monitoring and system upgrades. In this viewpoint, we take these issues into consideration and offer an integrated regulatory framework to AI developers, clinicians, researchers, and regulators, aiming to facilitate the adoption of AI that rests within the FDA's pathway, in research, development, and clinical medicine., Competing Interests: The authors declare no competing interests., (© 2021 The Author(s).)

Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the experiences of both clinicians and patients. We discuss key findings from a 2-year weekly effort to track and share key developments in medical AI. We cover prospective studies and advances in medical image analysis, which have reduced the gap between research and deployment. We also address several promising avenues for novel medical AI research, including non-image data sources, unconventional problem formulations and human-AI collaboration. Finally, we consider serious technical and ethical challenges in issues spanning from data scarcity to racial bias. As these challenges are addressed, AI's potential may be realized, making healthcare more accurate, efficient and accessible for patients worldwide., (© 2022. Springer Nature America, Inc.)

One of the biggest challenges of utilizing artificial intelligence (AI) in medicine is that physicians are reluctant to trust and adopt something that they do not fully understand and regarded as a "black box." Machine Learning (ML) can assist in reading radiological, endoscopic and histological pictures, suggesting diagnosis and predict disease outcome, and even recommending therapy and surgical decisions. However, clinical adoption of these AI tools has been slow because of a lack of trust. Besides clinician's doubt, patients lacking confidence with AI-powered technologies also hamper development. While they may accept the reality that human errors can occur, little tolerance of machine error is anticipated. In order to implement AI medicine successfully, interpretability of ML algorithm needs to improve. Opening the black box in AI medicine needs to take a stepwise approach. Small steps of biological explanation and clinical experience in ML algorithm can help to build trust and acceptance. AI software developers will have to clearly demonstrate that when the ML technologies are integrated into the clinical decision-making process, they can actually help to improve clinical outcome. Enhancing interpretability of ML algorithm is a crucial step in adopting AI in medicine., (© 2021 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.)

The digital information age has been a catalyst in creating a renewed interest in Artificial Intelligence (AI) approaches, especially the subclass of computer algorithms that are popularly grouped into Machine Learning (ML). These methods have allowed one to go beyond limited human cognitive ability into understanding the complexity in the high dimensional data. Medical sciences have seen a steady use of these methods but have been slow in adoption to improve patient care. There are some significant impediments that have diluted this effort, which include availability of curated diverse data sets for model building, reliable human-level interpretation of these models, and reliable reproducibility of these methods for routine clinical use. Each of these aspects has several limiting conditions that need to be balanced out, considering the data/model building efforts, clinical implementation, integration cost to translational effort with minimal patient level harm, which may directly impact future clinical adoption. In this review paper, we will assess each aspect of the problem in the context of reliable use of the ML methods in oncology, as a representative study case, with the goal to safeguard utility and improve patient care in medicine in general., (Copyright © 2021 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

In this introduction article, we summarize the 2020 International Conference on Intelligent Biology and Medicine (ICIBM 2020) conference which was held on August 9-10, 2020 (virtual conference). We then briefly describe the nine research articles included in this supplement issue. ICIBM 2020 hosted four scientific sections covering current topics in bioinformatics, computational biology, genomics, biomedical informatics, among others. A total of 75 original manuscripts were submitted to ICIBM 2020. All the papers were under rigorous review (at least three reviewers), and highly ranked manuscripts were selected for oral presentation and supplement issues. This genomics supplement issue included nine manuscripts. These articles cover methods and applications for single cell RNA sequencing, multi-omics data integration for gene regulation, gene fusion detection from long-read RNA sequencing, gene co-expression analysis of metabolic pathways in cancer, integrative genome-wide association studies (GWAS) of subcortical imaging phenotype in Alzheimer's disease, as well as deep learning methods for protein structure prediction, metabolic pathway membership inference, and horizontal gene transfer (HGT) insertion sites prediction.

Nowadays, artificial intelligence plays an integral role in medical and healthcare informatics. Developing an automatic question classification and answering system is essential for coping with constant advancements in science and technology. However, efficient online medical services are required to promote offline medical services. This article proposes a system that automatically classifies medical questions of patients into medical specialities and supports the Arabic language in the MENA region. Text classification is not trivial, especially when dealing with a highly morphologically complex language, the dialectical form of which is the dominant form on the Internet. This work utilizes 15,000 medical questions asked by the clients of Altibbi telemedicine company. The questions are classified into 15 medical specialities. As the number of medical questions received daily by the company has increased, a need has arisen for an automatic classification system that can save the medical personnel much time and effort. Therefore, this article presents an efficient medical speciality classification system based on swarm intelligence (SI) and an ensemble of support vector machines (SVMs). Particle swarm optimization (PSO) is an SI-based and stochastic metaheuristic algorithm that is adopted to search for the optimal number of features and tune the hyperparameters of the SVM classifiers, which are deployed as one-versus-rest for multi-class classification. In addition, PSO is integrated with various binarization techniques to boost its performance. The experimental results show that the proposed approach accomplished remarkable performance as it achieved an accuracy of 85% and a features reduction rate of 95.9%., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Historically, the term "artificial intelligence" dates to 1956 when it was first used in a conference at Dartmouth College in the US. Since then, the development of artificial intelligence has in part been shaped by the field of neuroscience. By understanding the human brain, scientists have attempted to build new intelligent machines capable of performing complex tasks akin to humans. Indeed, future research into artificial intelligence will continue to benefit from the study of the human brain. While the development of artificial intelligence algorithms has been fast paced, the actual use of most artificial intelligence (AI) algorithms in biomedical engineering and clinical practice is still markedly below its conceivably broader potentials. This is partly because for any algorithm to be incorporated into existing workflows it has to stand the test of scientific validation, clinical and personal utility, application context, and is equitable as well. In this context, there is much to be gained by combining AI and human intelligence (HI). Harnessing Big Data, computing power and storage capacities, and addressing societal issues emergent from algorithm applications, demand deploying HI in tandem with AI. Very few countries, even economically developed states, lack adequate and critical governance frames to best understand and steer the AI innovation trajectories in health care. Drug discovery and translational pharmaceutical research stand to gain from AI technology provided they are also informed by HI. In this expert review, we analyze the ways in which AI applications are likely to traverse the continuum of life from birth to death, and encompassing not only humans but also all animal, plant, and other living organisms that are increasingly touched by AI. Examples of AI applications include digital health, diagnosis of diseases in newborns, remote monitoring of health by smart devices, real-time Big Data analytics for prompt diagnosis of heart attacks, and facial analysis software with consequences on civil liberties. While we underscore the need for integration of AI and HI, we note that AI technology does not have to replace medical specialists or scientists and rather, is in need of such expert HI. Altogether, AI and HI offer synergy for responsible innovation and veritable prospects for improving health care from prevention to diagnosis to therapeutics while unintended consequences of automation emergent from AI and algorithms should be borne in mind on scientific cultures, work force, and society at large.

The International Association for Intelligent Biology and Medicine (IAIBM) is a nonprofit organization that promotes intelligent biology and medical science. It hosts an annual International Conference on Intelligent Biology and Medicine (ICIBM), which was established in 2012. The ICIBM 2019 was held from 9 to 11 June 2019 in Columbus, Ohio, USA. Out of the 105 original research manuscripts submitted to the conference, 18 were selected for publication in a Special Issue in Genes . The topics of the selected manuscripts cover a wide range of current topics in biomedical research including cancer informatics, transcriptomic, computational algorithms, visualization and tools, deep learning, and microbiome research. In this editorial, we briefly introduce each of the manuscripts and discuss their contribution to the advance of science and technology., Competing Interests: The authors declare no conflict of interest.

Process mining can provide greater insight into medical treatment processes and organizational processes in healthcare. To enhance comparability between processes, the quality of the labelled-data is essential. A literature review of the clinical case studies by Rojas et al. in 2016 identified several common aspects for comparison, which include methodologies, algorithms or techniques, medical fields, and healthcare specialty. However, clinical aspects are not reported in a uniform way and do not follow a standard clinical coding scheme. Further, technical aspects such as details of the event log data are not always described. In this paper, we identified 38 clinically-relevant case studies of process mining in healthcare published from 2016 to 2018 that described the tools, algorithms and techniques utilized, and details on the event log data. We then correlated the clinical aspects of patient encounter environment, clinical specialty and medical diagnoses using the standard clinical coding schemes SNOMED CT and ICD-10. The potential outcomes of adopting a standard approach for describing event log data and classifying medical terminology using standard clinical coding schemes are further discussed. A checklist template for the reporting of case studies is provided in the Appendix A to the article.

The fusion of artificial neural networks and fuzzy logic systems allows researchers to model real-world problems through the development of intelligent and adaptive systems. Artificial neural networks are able to adapt and learn by adjusting the interconnections between layers, while fuzzy logic inference systems provide a computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The combined use of those adaptive structures is known as "neuro-fuzzy" systems. In this paper, the basic elements of both approaches are analyzed, noticing that this blending could be applied for pattern recognition in medical applications.

A crucial bottleneck in medical artificial intelligence (AI) is high-quality labeled medical datasets. In this paper, we test a large variety of wisdom of the crowd algorithms to label medical images that were initially classified by individuals recruited through an app-based platform. Individuals classified skin lesions from the International Skin Lesion Challenge 2018 into 7 different categories. There was a large dispersion in the geographical location, experience, training, and performance of the recruited individuals. We tested several wisdom of the crowd algorithms of varying complexity from a simple unweighted average to more complex Bayesian models that account for individual patterns of errors. Using a switchboard analysis, we observe that the best-performing algorithms rely on selecting top performers, weighting decisions by training accuracy, and take into account the task environment. These algorithms far exceed expert performance. We conclude by discussing the implications of these approaches for the development of medical AI.

Diagnostic uncertainty is prevalent throughout medicine and significantly impacts patient care, especially when it goes unrecognized. However, we lack a reliable clinical means of identifying uncertainty. This study evaluates the narrative discourse within clinical notes in the Electronic Health Record as a means of identifying diagnostic uncertainty. Recognizing that discourse producers use language "semi-automatically" (Partington et al. 2013), we hypothesized that clinicians include distinct indications of uncertainty in their written assessments, which could be elucidated by linguistic analysis. Using a cohort of patients prospectively identified as having an uncertain diagnosis (UD), we conducted a detailed corpus-assisted discourse analysis. The analysis revealed a set of linguistic indicators constitutive of diagnostic uncertainty including terms of modality, register-specific terms, and linguistically identifiable clinical behaviours. This dictionary of UD indicators was thoroughly tested, and its performance was compared with a matched-control dataset. Based on the findings, we built a machine learning classification algorithm with the ability to predict UD patient cohorts with 87.0% accuracy, effectively demonstrating the feasibility of using clinical discourse to classify patients and directly impact the clinical environment.