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Background: The healthcare sector is rapidly integrating artificial intelligence-derived predictive models (AIDPM) to enhance clinical decision support, operational efficiency, and patient experiences. However, research on management strategies for AIDPM acquisition, deployment, and governance remains limited. This study examines changes in AIDPM integration and governance since 2021, with a particular focus on large language models and health equity considerations. Results: Our survey of health system leaders achieved a 49% response rate (32/65). While 84% of institutions reported using AIDPM in clinical practice, only 53% had established dedicated teams for these models. Compared to 2021, there was a significant increase in representation from experts in clinical informatics, operations, and quality improvement on AIDPM teams. Most organizations (41%) primarily purchased AIDPM from external vendors. Support for integrating large language models into healthcare practices was unanimous among respondents. The principal obstacles to AIDPM adoption included regulatory concerns, data security, workflow integration, and clinician acceptance. A large majority (72%) of respondents supported government regulation of AI in healthcare. While 76% of organizations reported having a team member dedicated to health equity, ethicists and diversity leaders were underrepresented on AIDPM teams (18%). Organizations reported various efforts to promote health equity, but involvement of frontline clinicians in AIDPM development and its impact on health equity was significantly less common. Conclusions: Clinical adoption of AIDPM faces challenges due to the absence of established best practices. Health system leaders strongly support federal regulations for AI in healthcare. These regulations could provide quality and safety standards. The study highlights the need for developing evaluation guidelines, especially for large language models. It also reveals a lack of uniform involvement of frontline clinicians and equity experts in AIDPM governance. Their involvement could increase adoption and trust of these new AI tools. Future research should assess healthcare systems' adherence to emerging regulations and best practice frameworks. This research should emphasize patient safety and health equity. These findings underscore the urgent need for a comprehensive roadmap. This roadmap would guide the responsible implementation of AIDPM in healthcare settings. [ABSTRACT FROM AUTHOR]

Background: The development and potential of artificial intelligence (AI) is remarkable. Its application in all medical disciplines, including rheumatology, is attracting attention. To what extent AI is already used in clinical routine in rheumatology is unknown. In addition, the perceived barriers, potentials, and expectations regarding AI by rheumatologists have not yet been studied. Objectives: To examine the current usage and perceived barriers and facilitators of AI, including large language models (LLMs), among rheumatologists. Design: National, observational, non-interventional, and cross-sectional web-based study. Methods: A web-based survey was developed by the Working Group Young Rheumatology (AGJR) of the German Society for Rheumatology. The survey was distributed at the Congress of the German Society for Rheumatology and via social media, QR code, and email from August 30 until November 4, 2023. Results: Responses from 172 rheumatologists (55% female; mean age 43 years) were analyzed. The majority stated that they did not previously use AI (73%) in their daily practice. Eighty-eight percent of rheumatologists rated their AI knowledge as low to intermediate and 84% would welcome dedicated training on LLMs. The majority of rheumatologists anticipated AI implementation to improve patient care (60%) and reduce daily workload (62%). Especially for diagnosis (73%), writing medical reports (70%), and data analysis (70%), rheumatologists reported a potential positive benefit of AI. Main AI concerns addressed the responsibility for medical decisions (64%) and data security (58%). Conclusion: Overall, the results indicate that rheumatologists currently have little AI knowledge and make very little use of AI in clinical routine. However, the majority of rheumatologists anticipate positive AI effects and would welcome increased AI implementation and dedicated training programs. [ABSTRACT FROM AUTHOR]

Background: To investigate the ability of artificial intelligence (AI)-based and semi-quantitative dynamic contrast enhanced (DCE) multiparametric MRI (mpMRI), performed within [18F]-PSMA-1007 PET/MRI, in differentiating benign from malignant prostate tissues in patients with primary prostate cancer (PC). Results: A total of seven patients underwent whole-body [18F]-PSMA-1007 PET/MRI examinations including a pelvic mpMRI protocol with T2w, diffusion weighted imaging (DWI) and DCE image series. Conventional analysis included visual reading of PET/MRI images and Prostate Imaging Reporting & Data System (PI-RADS) scoring of the prostate. On the prostate level, we performed manual segmentations for time-intensity curve parameter formation and semi-quantitative analysis based on DCE segmentation data of PC-suspicious lesions. Moreover, we applied a recently introduced deep learning (DL) pipeline previously trained on 1010 independent MRI examinations with systematic biopsy-enhanced histopathological targeted biopsy lesion ground truth in order to perform AI-based lesion detection, prostate segmentation and derivation of a deep learning PI-RADS score. DICE coefficients between manual and automatic DL-acquired segmentations were compared. On patient-based analysis, PET/MRI revealed PC-suspicious lesions in the prostate gland in 6/7 patients (Gleason Score-GS ≥ 7b) that were histologically confirmed. Four of these patients also showed lymph node metastases, while two of them had bone metastases. One patient with GS 6 showed no PC-suspicious lesions. Based on DCE segmentations, a distinction between PC-suspicious and normal appearing tissue was feasible with the parameters fitted maximum contrast ratio (FMCR) and wash-in-slope. DICE coefficients (manual vs. deep learning) were comparable with literature values at a mean of 0.44. Further, the DL pipeline could identify the intraprostatic PC-suspicious lesions in all six patients with clinically significant PC. Conclusion: Firstly, semi-quantitative DCE analysis based on manual segmentations of time-intensity curves was able to distinguish benign from malignant tissues. Moreover, DL analysis of the MRI data could detect clinically significant PC in all cases, demonstrating the feasibility of AI-supported approaches in increasing diagnostic certainty of PSMA-radioligand PET/MRI. [ABSTRACT FROM AUTHOR]

ardiovascular imaging has become the foundation of heart failure diagnostic studies. The most crucial technique for clinically diagnosing cardiac diseases is echocardiography. Depending on the positioning and angles of the probe, different cardiac views can be obtained during echocardiography. Therefore, the automatic classification of echo views, especially for computer systems and even automatic diagnosis in later stages, is the first step for echocardiogram diagnosis. In addition, the classification of heart views allows the tagging of echo videos to be done on a high scale and the possibility of database management and collection is provided. However, deep learning is an advanced machine learning method that is used to analyze both natural and medical images. But so far, it has not been widely used on cardiac ultrasound, the reason is the complexity of formats with multiple views and multi-view formats of echocardiogram. The proposed topic of this research is to provide novel and effective architectures for cardiac view classification. The aim of this study is to overcome the challenges in processing, categorizing and recognizing echo views stored as videos and images. In particular, in order to extract features, automatic methods and deep networks have replaced manual methods. In the presented solution, by using the transfer learning and the 3d-cnn method in image and video classification, we have improved the accuracy of echo views classification. [ABSTRACT FROM AUTHOR]

Background: The use of Cloud-based storage of personal health records has increased globally. The GPOC Series introduces the concept of a Global Patient co-Owned Cloud (GPOC) of personal health records. Here, we present the GPOC Series' international survey on the necessity of a GPOC. Methods: Online global survey with invitations sent to health ministries and major organisations. It received answers from health ministries and affiliated advisors of all 193 United Nations (UN) member states, 2 UN observer states (Holy See & Palestine) and 1 de facto UN non-member state (Taiwan) and from 18 major international organisations. The survey examined a dozen aspects encompassing demographics, privacy, sharing, movability, co-ownership, research, company usage, regulation and the necessity of a GPOC. Results: The GPOC Survey elicited responses from 267 individuals from 214 entities, including all UN member states, and major international organisations. Twelve domains were identified, covering demographics, correctness, privacy, commercial use, medical and non-medical research, co-ownership, data sharing, record movement, ownership centralisation, patient rights, environmental concerns, and foundation creation. Results show high agreement on most issues, including support for co-ownership (89%) and movement of personal health records (84%). Disagreement was prominent regarding centralised ownership by the state (64%) and data sharing without consent (85%). Additionally, respondents expressed interest in a neutral, decentralised foundation for regulation (73%) and the environmental sustainability of electronic health records (84%). Conclusions: A Global Patient co-Owned Cloud (GPOC) of personal health records could significantly enhance patient independence and involvement in health management, supported by the near consensus agreement across various domains identified in our survey. This consensus underscores the potential of GPOC to democratise healthcare and align with UN Sustainable Development Goals (SDGs). The survey results demonstrate strong support for GPOC's role in promoting evidence-based patient management, reducing information silos, and fostering ethical data sharing. Moreover, the overwhelming agreement on key principles of co-ownership, data sharing, and environmental sustainability highlights the global inclination for a decentralised, patient-controlled PHR platform. This platform stands to empower patients worldwide, advance precision medicine, and contribute to the global development and dissemination of artificial intelligence in healthcare. [ABSTRACT FROM AUTHOR]

The utilization of pharmaceuticals in medical and veterinary treatment has not only improved human and animal health but has also boosted food-production and economic welfare. However, the release of pharmaceuticals in the environment through various pathways, such as manufacturing, human excretion, and substandard disposal, can have detrimental effects on ecosystems and various biological entities associated with these systems. High levels of pharmaceutical residues have been detected further downstream of manufacturing facilities, and untreated veterinary medication leftovers can end up in waterbodies. Methods utilizing artificial intelligence (AI) and machine learning (ML) have been employed to establish connections between chemical structure and biological activity, referred to as quantitative structure--activity relationships (QSARs) for the compounds. QSAR models use chemical structures to predict hazardous activity when experimental data is lacking, thereby helping prioritize chemicals for testing and compilation. Combinatorial chemistry, by enabling high-throughput compound synthesis, accelerates the generation of targeted molecules for testing across various fields. Green chemistry helps in creating, designing, and implementing chemical products and procedures with the aim of minimizing or eradicating the generation and subsequent utilization of harmful substances. In addition, pharmaceutical sensor technologies (PST) are critical tools in modern medicine, enabling precise detection and monitoring of various biochemical and physiological markers and parameters. The synergy between AI, ML, QSAR modeling, and the implementation of combinatorial and green chemistry methodologies is pivotal in driving the development of innovative products and PST in pharmaceutics. This interdisciplinary approach is crucial for creating solutions with reduced toxicity in pharmaceutical processes, thereby ensuring enhanced public safety and promoting the sustainability of environmental resources. By integrating these advanced methodologies, the pharmaceutical industry can achieve greater detection accuracy, efficiency in production of eco-friendly products, ultimately leading to safer pharmaceutics and a healthier planet. [ABSTRACT FROM AUTHOR]

This paper examines the transformative impact of artificial intelligence (AI) on pharmacogenomics, signaling a paradigm shift in personalized medicine. With a focus on enhancing drug response prediction and treatment optimization, AI, particularly machine learning and deep learning algorithms, navigates the complexity of genomic data. By elucidating intricate relationships between genetic factors and drug responses, AI augments the identification of genetic markers and contributes to the development of comprehensive models. The review emphasizes AI's role in guiding treatment decisions, minimizing adverse reactions, and optimizing drug dosages in clinical settings. Ethical considerations, challenges, and future directions are also discussed. This work underscores the synergy of AI and pharmacogenomics, offering a more effective and patient-centric approach to drug therapy, marking a significant advancement in the field of personalized medicine. [ABSTRACT FROM AUTHOR]

Background: The use of Cloud-based storage personal health records has increased globally. The GPOC series introduces the concept of a Global Patient co-Owned Cloud (GPOC) of personal health records. Technical sandboxes allow the capability to simulate different scientific concepts before making them production ready. None exist for the medical fields and cloud-based research. Methods: We constructed and tested the sandbox using open-source infrastructures (Ubuntu, Alpine Linux, and Colaboratory) and demonstrated it on a cloud platform. Data preprocessing utilised standard and in-house libraries. The Mina protocol, implementing zero-knowledge proofs, ensured secure blockchain operations, while the Ethereum smart contract protocol within Hyperledger Besu supported enterprise-grade sandbox development. Results: Here, we present the GPOC series' technical sandbox. This is to facilitate future online research and testing of the concept and its security, encryption, movability, research potential, risks and structure. It has several protocols for homomorphic encryption, decentralisation, transfers, and file management. The sandbox is openly available online and tests authorisation, transmission, access control, and integrity live. It invites all committed parties to test and improve the platform. Individual patients, clinics, organisations and regulators are invited to test and develop the concept. The sandbox displays co-ownership of personal health records. Here it is trisected between patients, clinics and clinicians. Patients can actively participate in research and control their health data. The challenges include ensuring that a unified underlying protocol is maintained for cross-border delivery of care based on data management regulations. Conclusions: The GPOC concept, as demonstrated by the GPOC Sandbox, represents an advancement in healthcare technology. By promoting patient co-ownership and utilising advanced technologies like blockchain and homomorphic encryption, the GPOC initiative enhances individual control over health data and facilitates collaborative medical research globally. The justification for this research lies in its potential to improve evidence-based medicine and AI dissemination. The significance of the GPOC initiative extends to various aspects of healthcare, patient co-ownership of health data, promoting access to resources and healthcare democratisation. The implications include better global health outcomes through continued development and collaboration, ensuring the successful adoption of the GPOC Sandbox and advancing innovation in digital health. [ABSTRACT FROM AUTHOR]

Background: We attempt to understand our patients' awareness and perception about robot-assisted surgery (RAS), to be able to educate them better and help them make informed decisions. Methods: Patients who visited the Obstetrics & Gynaecology out-patient department were given questionnaires. Two hundred and sixty-two completed questionnaires were analysed. Results: Comfort with technology was significantly associated with the patients' knowledge of the robot's appearance. Correct identification of the meaning of RAS, the surgeon's role, and appearance of the robot were also individually associated with preference towards RAS. Those who had undergone RAS before favoured it significantly more than those who did not have a history of RAS. Conclusion: Counselling is important to help patients get over misinformation. Patients need to be explained that the robot does not have autonomy. The three main concepts to be focused on are—meaning of robotic surgery, surgeon's position/role during the surgery, and appearance of the robot. Limitations also need to be discussed. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has emerged as a transformative force in various sectors, offering both opportunities and challenges, particularly in a rapidly developing nation like India. This paper explores the dual aspects of AI as a boon and a bane, analysing its impact on economic growth, employment, healthcare, education, and ethical concerns. While AI promises enhanced productivity and innovation, it also poses risks such as job displacement, privacy violations, and algorithmic bias. This study aims to provide a balanced perspective on the implications of AI in India, highlighting the need for strategic frameworks that maximize benefits while mitigating risks. [ABSTRACT FROM AUTHOR]

Pure laparoscopic donor hepatectomy (PLDH) has become a standard practice for living donor liver transplantation in expert centers. Accurate understanding of biliary structures is crucial during PLDH to minimize the risk of complications. This study aims to develop a deep learning-based segmentation model for real-time identification of biliary structures, assisting surgeons in determining the optimal transection site during PLDH. A single-institution retrospective feasibility analysis was conducted on 30 intraoperative videos of PLDH. All videos were selected for their use of the indocyanine green near-infrared fluorescence technique to identify biliary structure. From the analysis, 10 representative frames were extracted from each video specifically during the bile duct division phase, resulting in 300 frames. These frames underwent pixel-wise annotation to identify biliary structures and the transection site. A segmentation task was then performed using a DeepLabV3+ algorithm, equipped with a ResNet50 encoder, focusing on the bile duct (BD) and anterior wall (AW) for transection. The model's performance was evaluated using the dice similarity coefficient (DSC). The model predicted biliary structures with a mean DSC of 0.728 ± 0.01 for BD and 0.429 ± 0.06 for AW. Inference was performed at a speed of 15.3 frames per second, demonstrating the feasibility of real-time recognition of anatomical structures during surgery. The deep learning-based semantic segmentation model exhibited promising performance in identifying biliary structures during PLDH. Future studies should focus on validating the clinical utility and generalizability of the model and comparing its efficacy with current gold standard practices to better evaluate its potential clinical applications. [ABSTRACT FROM AUTHOR]

The article present news along with the case study related to ophthalmology. Topics include the Taiwan Macula Society (TMS), which was founded to enhance patient care and support vitreoretinal specialists; role of artificial intelligence in transforming clinical retina practice; complex case involving a rigid intraocular lens (IOL) in the capsular sulcus, an open posterior capsule, choroidal detachment & a dropped IOL; and case of pseudopapilledema caused by leukemic optic nerve infiltration.

Abstract Background The use of Cloud-based storage of personal health records has increased globally. The GPOC Series introduces the concept of a Global Patient co-Owned Cloud (GPOC) of personal health records. Here, we present the GPOC Series’ international survey on the necessity of a GPOC. Methods Online global survey with invitations sent to health ministries and major organisations. It received answers from health ministries and affiliated advisors of all 193 United Nations (UN) member states, 2 UN observer states (Holy See & Palestine) and 1 de facto UN non-member state (Taiwan) and from 18 major international organisations. The survey examined a dozen aspects encompassing demographics, privacy, sharing, movability, co-ownership, research, company usage, regulation and the necessity of a GPOC. Results The GPOC Survey elicited responses from 267 individuals from 214 entities, including all UN member states, and major international organisations. Twelve domains were identified, covering demographics, correctness, privacy, commercial use, medical and non-medical research, co-ownership, data sharing, record movement, ownership centralisation, patient rights, environmental concerns, and foundation creation. Results show high agreement on most issues, including support for co-ownership (89%) and movement of personal health records (84%). Disagreement was prominent regarding centralised ownership by the state (64%) and data sharing without consent (85%). Additionally, respondents expressed interest in a neutral, decentralised foundation for regulation (73%) and the environmental sustainability of electronic health records (84%). Conclusions A Global Patient co-Owned Cloud (GPOC) of personal health records could significantly enhance patient independence and involvement in health management, supported by the near consensus agreement across various domains identified in our survey. This consensus underscores the potential of GPOC to democratise healthcare and align with UN Sustainable Development Goals (SDGs). The survey results demonstrate strong support for GPOC’s role in promoting evidence-based patient management, reducing information silos, and fostering ethical data sharing. Moreover, the overwhelming agreement on key principles of co-ownership, data sharing, and environmental sustainability highlights the global inclination for a decentralised, patient-controlled PHR platform. This platform stands to empower patients worldwide, advance precision medicine, and contribute to the global development and dissemination of artificial intelligence in healthcare.

Abstract Pure laparoscopic donor hepatectomy (PLDH) has become a standard practice for living donor liver transplantation in expert centers. Accurate understanding of biliary structures is crucial during PLDH to minimize the risk of complications. This study aims to develop a deep learning-based segmentation model for real-time identification of biliary structures, assisting surgeons in determining the optimal transection site during PLDH. A single-institution retrospective feasibility analysis was conducted on 30 intraoperative videos of PLDH. All videos were selected for their use of the indocyanine green near-infrared fluorescence technique to identify biliary structure. From the analysis, 10 representative frames were extracted from each video specifically during the bile duct division phase, resulting in 300 frames. These frames underwent pixel-wise annotation to identify biliary structures and the transection site. A segmentation task was then performed using a DeepLabV3+ algorithm, equipped with a ResNet50 encoder, focusing on the bile duct (BD) and anterior wall (AW) for transection. The model’s performance was evaluated using the dice similarity coefficient (DSC). The model predicted biliary structures with a mean DSC of 0.728 ± 0.01 for BD and 0.429 ± 0.06 for AW. Inference was performed at a speed of 15.3 frames per second, demonstrating the feasibility of real-time recognition of anatomical structures during surgery. The deep learning-based semantic segmentation model exhibited promising performance in identifying biliary structures during PLDH. Future studies should focus on validating the clinical utility and generalizability of the model and comparing its efficacy with current gold standard practices to better evaluate its potential clinical applications.

Artificial Intelligence (AI) has an increasing role to play in medical education and has great potential to revolutionize health professional education systems overall. However, this is accompanied by substantial questions concerning technical and ethical risks which are of particular importance because the quality of medical education has a direct effect on physical and psychological health and wellbeing. This article establishes an overarching distinction of AI across two typological dimensions, functional and humanistic. As indispensable foundations, these are then related to medical practice overall, and forms of implementation with examples are described in both general and medical education. Increasingly, the conditions for successful medical education will depend on an understanding of AI and the ethical issues surrounding its implementation, as well as the formulation of appropriate guidelines by regulatory and other authorities. Within that discussion, the limits of both narrow or Routine AI (RAI) and artificial general intelligence or Decision AI (DAI) are examined particularly in view of the ethical need for Trustworthy AI (TAI) as part of the humanistic dimension. All stakeholders, from patients to medical practitioners, managers, and institutions, need to be able to trust AI, and loss of confidence could be catastrophic in some cases.

Background: During the last thirty years the world has benefited from the increasing adoption to cloud-based storage of personal health records. The concept of a Global Patient co-Owned Cloud (GPOC) of personal health records is presented in the GPOC Series, which contains a systematic review and meta-analysis, a global survey among 100% of the UN member states and a technical sandbox environment. GPOC contains patient co-ownership of personal health records. The global survey showed a consensus for the realisation of a GPOC. Methods: Here, we present a Delphi styled GPOC Summit. It also contains a final vote, that validates the results of the previous GPOC Survey. Hence, the Summit contains both multiple rounds of discussion and a voting. Results: At the GPOC Summit a unanimous consensus for patient co-ownership of the personal health records was expressed. The majority of participants underlined that GPOC may benefit global health, transfers of the personal health records globally and the dissemination of evidence-based medicine across the globe. The plausible advantages for patients, both refugees and travellers and for developing economies were underlined. GPOC, as a large substrate for global medical artificial intelligence research, was highlighted. There was unanimous positive interest for GPOC. The GPOC Summit agreed on patient co-ownership of personal health records. This collective agreement underscores the potential of GPOC to enhance global health outcomes, facilitate seamless exchange of health data, and advance medical research worldwide. Conclusions: The summit emphasised the potential benefits of GPOC for diverse populations and economies, highlighting its potential role in improving healthcare access and infrastructure. Moving forward, coordination with relevant initiatives could enhance interoperability and standardisation in healthcare data management. In conclusion, the GPOC Summit represents one step towards realising a patient-centric, globally accessible healthcare ecosystem, with possible implications for the development and global adoption of AI-driven healthcare solutions. [ABSTRACT FROM AUTHOR]

Over the last decades, the use of artificial intelligence, machine learning and deep learning in medical fields has skyrocketed. Well known for their results in segmentation, motion management and posttreatment outcome tasks, investigations of machine learning and deep learning models as fast dose calculation or quality assurance tools have been present since 2000. The main motivation for this increasing research and interest in artificial intelligence, machine learning and deep learning is the enhancement of treatment workflows, specifically dosimetry and quality assurance accuracy and time points, which remain important time-consuming aspects of clinical patient management. Since 2014, the evolution of models and architectures for dose calculation has been related to innovations and interest in the theory of information research with pronounced improvements in architecture design. The use of knowledge-based approaches to patient-specific methods has also considerably improved the accuracy of dose predictions. This paper covers the state of all known deep learning architectures and models applied to external radiotherapy with a description of each architecture, followed by a discussion on the performance and future of deep learning predictive models in external radiotherapy. [ABSTRACT FROM AUTHOR]

This study aimed to design an autodelineation model based on convolutional neural networks for generating high-risk clinical target volumes and organs at risk in image-guided adaptive brachytherapy for cervical cancer. A novel SERes-u-net was trained and tested using CT scans from 98 patients with locally advanced cervical cancer who underwent image-guided adaptive brachytherapy. The Dice similarity coefficient, 95th percentile Hausdorff distance, and clinical assessment were used for evaluation. The mean Dice similarity coefficients of our model were 80.8%, 91.9%, 85.2%, 60.4%, and 82.8% for the high-risk clinical target volumes, bladder, rectum, sigmoid, and bowel loops, respectively. The corresponding 95th percentile Hausdorff distances were 5.23 mm, 4.75 mm, 4.06 mm, 30.0 mm, and 20.5 mm. The evaluation results revealed that 99.3% of the convolutional neural networks-generated high-risk clinical target volumes slices were acceptable for oncologist A and 100% for oncologist B. Most segmentations of the organs at risk were clinically acceptable, except for the 25% sigmoid, which required significant revision in the opinion of oncologist A. There was a significant difference in the clinical evaluation of convolutional neural networks-generated high-risk clinical target volumes between the two oncologists (P < 0.001), whereas the score differences of the organs at risk were not significant between the two oncologists. In the consistency evaluation, a large discrepancy was observed between senior and junior clinicians. About 40% of SERes-u-net-generated contours were thought to be better by junior clinicians. The high-risk clinical target volumes and organs at risk of cervical cancer generated by the proposed convolutional neural networks model can be used clinically, potentially improving segmentation consistency and efficiency of contouring in image-guided adaptive brachytherapy workflow. [ABSTRACT FROM AUTHOR]

Background and Objectives: Integrating large language models (LLMs) such as GPT-4 Turbo into diagnostic imaging faces a significant challenge, with current misdiagnosis rates ranging from 30–50%. This study evaluates how prompt engineering and confidence thresholds can improve diagnostic accuracy in neuroradiology. Methods: We analyze 751 neuroradiology cases from the American Journal of Neuroradiology using GPT-4 Turbo with customized prompts to improve diagnostic precision. Results: Initially, GPT-4 Turbo achieved a baseline diagnostic accuracy of 55.1%. By reformatting responses to list five diagnostic candidates and applying a 90% confidence threshold, the highest precision of the diagnosis increased to 72.9%, with the candidate list providing the correct diagnosis at 85.9%, reducing the misdiagnosis rate to 14.1%. However, this threshold reduced the number of cases that responded. Conclusions: Strategic prompt engineering and high confidence thresholds significantly reduce misdiagnoses and improve the precision of the LLM diagnostic in neuroradiology. More research is needed to optimize these approaches for broader clinical implementation, balancing accuracy and utility. [ABSTRACT FROM AUTHOR]

With the improvement of economic conditions and the increase in living standards, people's attention in regard to health is also continuously increasing. They are beginning to place their hopes on machines, expecting artificial intelligence (AI) to provide a more humanized medical environment and personalized services, thus greatly expanding the supply and bridging the gap between resource supply and demand. With the development of IoT technology, the arrival of the 5G and 6G communication era, and the enhancement of computing capabilities in particular, the development and application of AI-assisted healthcare have been further promoted. Currently, research on and the application of artificial intelligence in the field of medical assistance are continuously deepening and expanding. AI holds immense economic value and has many potential applications in regard to medical institutions, patients, and healthcare professionals. It has the ability to enhance medical efficiency, reduce healthcare costs, improve the quality of healthcare services, and provide a more intelligent and humanized service experience for healthcare professionals and patients. This study elaborates on AI development history and development timelines in the medical field, types of AI technologies in healthcare informatics, the application of AI in the medical field, and opportunities and challenges of AI in the field of medicine. The combination of healthcare and artificial intelligence has a profound impact on human life, improving human health levels and quality of life and changing human lifestyles. [ABSTRACT FROM AUTHOR]

This paper discusses the ethical implications of deploying artificial Intelligence (AI) in Pakistan's healthcare sector policies. It identifies critical issues around which an AI ethic should be formulated, including data privacy, algorithmic bias, and potential benefits that could emerge from using AI among patients receiving care. This study will explore ethical frameworks and case studies of AI applications within Pakistani hospitals and how they can be compared with global standards to understand the challenges faced when introducing Artificial Intelligence into Pakistan healthcare. The results underscore the urgent need to set up solid ethical frameworks and guidelines so that AI becomes a responsible tool for superior-quality healthcare services and rendering efficient resource management. [ABSTRACT FROM AUTHOR]

As the use of Artificial Intelligence (AI) technologies in healthcare is expanding, patients in the European Union (EU) are increasingly subjected to automated medical decision-making. This development poses challenges to the protection of patients' rights. A specific patients' right not to be subject to automated medical decision-making is not considered part of the traditional portfolio of patients' rights. The EU AI Act also does not contain such a right. The General Data Protection Regulation (GDPR) does, however, provide for the right 'not to be subject to a decision based solely on automated processing' in Article 22. At the same time, this provision has been severely critiqued in legal scholarship because of its lack of practical effectiveness. However, in December 2023, the Court of Justice of the EU first provided an interpretation of this right in C-634/21 (SCHUFA)—although in the context of credit scoring. Against this background, this article provides a critical analysis of the application of Article 22 GDPR to the medical context. The objective is to evaluate whether Article 22 GDPR may provide patients with the right to refuse automated medical decision-making. It proposes a health-conformant reading to strengthen patients' rights in the EU. [ABSTRACT FROM AUTHOR]

Background. The analysis, interpretation and storage of information is entrusted to the individual expert, who bases his judgments on the knowledge resulting from the experience. The aim of this experimental study is to analyse and introduce a new line of research applicable to forensic pathology, based on the use of artificial intelligence techniques as a possible tool for data collection and analysis. Methods. The sample analysed is represented by judicial autopsies performed at the University of Catanzaro from 01/01/2020 to 31/12/2021. For each case were performed: study of medical records; autopsy; histological examinations; toxicological analysis of blood samples. Continuous variables were presented as means ± standard deviations, and categorical variables were expressed as percentages. A random forest regression model was conducted, as a machine learning approach, to estimate the importance of individual solid organ weight variables in predicting cause of death. Conclusions. This study aimed to evaluate autopsy data to aid in the description and study of forensic cases, using a machine learning approach. To date, this study appears to be the first to evaluate the weight of organs in predicting a cause of death. Artificial intelligence techniques are an optimal solution in solving forensic dilemmas. The results of this study demonstrate that routine data can be submitted using machine learning techniques in order to identify key elements of procedures that provide more information in relation to the prediction of cause of death. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence in the healthcare sector is becoming widespread for reasons such as analyzing digital patient data, including it in decision-making processes, improving the quality of healthcare services, and providing cost, time, and access advantages. This study aims to evaluate published articles on bibliometric indicators and the use of artificial intelligence in the healthcare sector and examine the content of the most cited articles. Articles about artificial intelligence in the health sector in the Web of Science database were included in the study using the criteria of "keyword, publication year, and publication language". The research covers 2,680 articles published in English by 14,195 authors from 106 countries in 1084 journals between 2020-2024. 4,671 different keywords were used in the published articles. The country that published the most was "USA", the journal was "Journal of Medical Internet Research", the author was "Meng Ji", and the most cited author was "Weihua Li". The 55 most cited (≥50) articles focused on themes related to "diagnosis of COVID-19 disease", "diagnosis of diseases", "detection and classification of cancerous cells", "identification of disease risk factors and disease prediction", "prediction of treatment outcomes", "prediction of disease course", "personalized treatment recommendations", "decision-making processes", "ethical considerations, risks, and responsibilities". With the COVID-19 pandemic, it is seen that the number of articles on artificial intelligence in the healthcare sector has increased exponentially. In the research, articles related to artificial intelligence in the health sector were examined, and a framework was created for researchers by revealing the most publishing countries, journals, authors, most cited authors, and keywords that were used the most. [ABSTRACT FROM AUTHOR]

Cardiovascular disease remains a leading cause of premature mortality and disability globally, necessitating early and effective diagnostic imaging strategies to enhance population health and quality of life. This study explores the utilization of various cardiovascular imaging modalities--including echocardiography, nuclear imaging, computed tomography (CT), magnetic resonance imaging (MRI), and invasive catheterization--as preventative measures to identify cardiovascular issues before they escalate. Each imaging technique offers unique advantages, from the noninvasive, high-resolution capabilities of echocardiography to the detailed anatomical insights provided by cardiac MRI and the real-time analysis possible with catheterization. However, the limitations and associated risks, such as exposure to radiation and contrast-induced nephropathy, must be carefully considered. Furthermore, the integration of artificial intelligence (AI), machine learning (ML), and deep learning (DL) into cardiovascular imaging is emerging as a transformative frontier, enabling enhanced prognostication and diagnostic accuracy. This article underscores the importance of adopting multimodal imaging approaches, emphasizing the need for ongoing research to improve imaging protocols and the application of AI technologies to optimize patient outcomes and streamline clinical decision-making in cardiovascular care. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is rapidly transforming the landscape of nephrology, offering innovative solutions that enhance diagnosis, treatment, and patient care. This literature review explores the current and potential applications of AI across various domains within nephrology. We discuss AI-driven advancements in early diagnosis, personalized treatment planning, renal replacement therapy, and transplant nephrology. Furthermore, we examine how AI enhances patient care through remote monitoring, telehealth, and virtual assistants. While the promise of AI is immense, this review also addresses the ethical, regulatory, and technical challenges that accompany its integration into clinical practice. By highlighting the transformative potential of AI in nephrology, we underscore the need for continued research and collaboration to fully realize its benefits in improving kidney health outcomes. [ABSTRACT FROM AUTHOR]

Background/Aim: The pathological diagnosis of organizing pneumonia (OP) relies on conventional traditional histopathological analysis, which involves examining stained thin slices of tissue. However, this method often results in suboptimal diagnostic objectivity due to low tissue sampling rates. This study aimed to assess the efficacy of tissue-clearing and infiltration-enhanced 3D spatial imaging techniques for elucidating the tissue architecture of OP. Materials and Methods: H&E staining, 3D imaging technology, and AIassisted analysis were employed to facilitate the construction of a multidimensional tissue architecture using six OP patient specimens procured from Taichung Veterans General Hospital, enabling a comprehensive morphological assessment. Results: Specimens underwent H&E staining and exhibited Masson bodies and varying degrees of interstitial fibrosis. Furthermore, we conducted a comprehensive study of 3D images of the pulmonary histology reconstructed through an in-depth pathology analysis, and uncovered heterogenous distributions of fibrosis and Masson bodies across different depths of the OP specimens. Conclusion: Integrating 3D imaging for OP with AI-assisted analysis permits a substantially enhanced visualization and delineation of complex histological pulmonary disorders such as OP. The synergistic application of conventional histopathology with novel 3D imaging elucidated the sophisticated spatial configuration of OP, revealing the presence of Masson bodies and interstitial fibrosis. This methodology transcends conventional pathology constraints and paves the way for advanced algorithmic approaches to enhance precision in the detection, classification, and clinical management of lung pathologies. [ABSTRACT FROM AUTHOR]

Background/Aim: Demographic change and increasing complexity of therapy decisions lead to a growing burden on the healthcare system, necessitating efforts to simplify and enhance the efficiency of patient care. The present study evaluates ChatGPT's ability to provide therapy recommendations for gynecological malignancies that are both in line with the local guidelines and individually tailored to the patient. Patients and Methods: Sixteen patients with endometrial, cervical, and ovarian cancer who were treated in the gynecological clinic of the University Hospital Erlangen from January 2022 to August 2023 were included in the analysis. Data collected within clinical routine care were communicated to the chat-based AI model ChatGPT (version 3.5). ChatGPT's performance generating treatment plans were evaluated using an answer scoring system and descriptive analysis. Results: According to the answer scoring system [range: -1 point (minimum) to 2 points (maximum)], ChatGPT demonstrated a good potential in generating therapy recommendations with an average score of 0.75 points for patients with ovarian cancer, 0.7 points for cervical and 1.5 points for endometrial cancer patients. The most common deductions in points were about incomplete therapy recommendations, whereas contraindicated treatment modalities were rarely suggested. Individual patient characteristics were regularly considered by ChatGPT. ChatGPT reliably indicated aftercare and provided detailed information on preventive measures as well as supportive treatment. Conclusion: ChatGPT is a promising tool for the generation of therapy suggestions for gynecological carcinomas with high flexibility in response to individual patient differences. At the current state, however, ChatGPT is not suitable for replacing expert panels. [ABSTRACT FROM AUTHOR]

The accelerated discovery and production of pharmaceutical products has resulted in many positive outcomes. However, this progress has also contributed to problematic polypharmacy, one of the rapidly growing threats to public health in this century. Problematic polypharmacy results in adverse patient outcomes and imposes increased strain and financial burden on healthcare systems. A review was conducted on the current body of evidence concerning factors contributing to and consequences of problematic polypharmacy. Recent trials investigating interventions that target polypharmacy and emerging solutions, including incorporation of artificial intelligence, are also examined in this article. To shift away from problematic polypharmacy, a multifaceted interdisciplinary approach is necessary. Any potentially successful strategy must be adapted to suit various healthcare settings and must utilize all available resources, including artificial intelligence. [ABSTRACT FROM AUTHOR]

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes. With the development of intelligent diagnosis in traditional Chinese medicine (TCM), artificial intelligence (AI) can improve the accuracy and efficiency of eye diagnosis. However, the research on intelligent eye diagnosis still faces many challenges, including the lack of standardized and precisely labeled data, multi-modal information analysis, and artificial intelligence models for syndrome differentiation. The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelligence. This study elaborates on the three key technologies of AI models in the intelligent application of TCM eye diagnosis, and explores the implications for the research of eye diagnosis intelligence. First, a database concerning eye diagnosis was established based on self-supervised learning so as to solve the issues related to the lack of standardized and precisely labeled data. Next, the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis. Last, the building of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome differentiation models. In summary, research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications. [ABSTRACT FROM AUTHOR]

Background: Due to the importance of evidence-based research in plastic surgery, the authors of this study aimed to assess the accuracy of ChatGPT in generating novel systematic review ideas within the field of craniofacial surgery. Methods: ChatGPT was prompted to generate 20 novel systematic review ideas for 10 different subcategories within the field of craniofacial surgery. For each topic, the chatbot was told to give 10 "general" and 10 "specific" ideas that were related to the concept. In order to determine the accuracy of ChatGPT, a literature review was conducted using PubMed, CINAHL, Embase, and Cochrane. Results: In total, 200 total systematic review research ideas were generated by ChatGPT. We found that the algorithm had an overall 57.5% accuracy at identifying novel systematic review ideas. ChatGPT was found to be 39% accurate for general topics and 76% accurate for specific topics. Conclusion: Craniofacial surgeons should use ChatGPT as a tool. We found that ChatGPT provided more precise answers with specific research questions than with general questions and helped narrow down the search scope, leading to a more relevant and accurate response. Beyond research purposes, ChatGPT can augment patient consultations, improve healthcare equity, and assist in clinical decisionmaking. With rapid advancements in artificial intelligence (AI), it is important for plastic surgeons to consider using AI in their clinical practice to improve patient-centered outcomes. [ABSTRACT FROM AUTHOR]

Background: The growing significance of social media in commercial enterprises is bringing this theme to the attention of decision-makers. These days, businesses use Facebook, Twitter, and YouTube as part of their marketing strategies. This encourages communication between consumers and marketers. Similar communication tactics are used in the pharmaceutical sector. However, because this is a healthcare-related industry, there are a lot of rules that apply to it, especially to its marketing department. Purpose: The purpose of this study is to assess the pharmaceutical industry's online presence on social media sites like Facebook, Twitter, and YouTube, as well as to describe the various digital engagement tactics that are employed. Conclusion: The study's conclusions indicate that not all pharmaceutical businesses use social media, and that certain platforms are more popular than others. It's interesting to note that different social media platforms underwent different digital engagement techniques, and that the level of involvement was unrelated to the size of the companies. This study offers insights into the social media organization of pharmaceutical businesses and ostensibly supplies a framework and technique for further research in this area. Furthermore, a few of the constraints found offer guidance for future research directions. [ABSTRACT FROM AUTHOR]

Nearly 1% of people worldwide suffer from epilepsy. Electroencephalogram (EEG)-based diagnostics and monitoring tools, such as scalp EEG, subscalp EEG, stereo EEG, or sub/epi-dural EEG recordings [also known as electrocorticography (ECoG)], are widely used in different settings as the gold standard techniques to perform seizure identification, localization, and more primarily in epilepsy or suspected epilepsy in patients. Techniques such as subscalp EEG and ECoG offer long-term brain interaction, potentially replacing traditional electroceuticals with smart closed-loop therapies. However, these systems require continuous on-device training due to real-time demands and high power consumption. Inspired by the brain architecture, biologically plausible algorithms, such as some neuromorphic computing, show promise in addressing these challenges. In our research, we utilized liquid time-constant spiking neural networks with forward propagation through time to detect seizures in scalp-EEG. We trained and validated our model on the Temple University Hospital dataset and tested its generalization on out-of-sample data from the Royal Prince Alfred Hospital (RPAH) and EPILEPSIAE datasets. Our model achieved high area under the receiver operating characteristic curve (AUROC) scores of 0.83 in both datasets. We assessed the robustness by decreasing the memory size by 90% and obtained an overall AUROC of 0.82 in the RPAH dataset and 0.83 in the EPILEPSIAE dataset. Our model showed outstanding results of 3.1 μJ power consumption per inference and a 20% firing rate during training. This allows for incorporating bio-inspired efficient algorithms for on-device training, tackling challenges such as memory, power consumption, and efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Croatian Nursing Journal is the property of University of Applied Health Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The human race is forced to engage in a very rapid adaptation process whenever it is confronted with technological change in any sphere of life. The unabated progress of artificial intelligence (AI) has also impacted the field of critical thinking. It is fascinating that critical thinking, an essential component of intellectual intelligence in nursing, seems to be disrupted by an artificial brainalike machine that can automatically analyze and synthesize a series of contexts. This has been an improvisation of ideas of intellectual intelligence for a very long time. Perspectives on both sides of the coin bring up interesting questions about the role that AI will play in the future, such as whether it will disrupt the critical thinking skills of nurses or whether it may be engaged as a tool to increase the critical thinking skills capability, especially in the fields of nursing care. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Engineering Research & Development (IJERAD) is the property of International Journal of Engineering Research & Development and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

At a time of a rapid growth in the population of elderly individuals and at a time of decreased/pressed availability of human healthcare-resources, automated face analysis has the potential to offer efficient and cost-effective methods for monitoring of a number of pathologies. The author revisits works in automated face analysis, which have focused on designing computer vision algorithms deducing the health state of individuals. Current limitations and benefits are discussed, placing emphasis on the potential that such technology can bring. Computer vision algorithms, most recently based on deep neural networks have been trained with facial images or videos, jointly with health state annotations from clinical experts, in order to learn such algorithms to deduce facets of health states. Examples of such notable algorithms include approaches detecting stress, depression, apathy, pain, neurological disorder, as well as classification of expressions and phenotypes of genetic disorders. Such algorithms are evolving rapidly, providing increasingly reliable accuracy and can support clinicians by providing objective measures. [ABSTRACT FROM AUTHOR]

With a shortage of oncologists predicted and an increase in demand for oncology services amid a growing aging population, the need for innovation is crucial. Artificial intelligence (AI) presents a promising solution, offering enhanced efficiency in various aspects of cancer care, from analyzing imaging to personalized treatment planning. The American Oncology Network (AON) has taken the initiative in integrating AI, utilizing platforms such as Flatiron Assist and VieCure to aid clinicians in decision-making processes, treatment planning, and patient management. Further, AON addresses challenges in data management with its subsidiary Meaningful Insights Biotech Analytics (MiBA). By transforming unstructured oncology data into actionable insights using AI, MiBA supports various clinical functions and helps optimize therapies. Despite the benefits, challenges such as data privacy, algorithm development, and ethical considerations remain. Ensuring the quality and integrity of data used for AI algorithms is crucial, as is addressing concerns regarding accountability and algorithm bias. Looking ahead, AI holds immense potential to revolutionize oncology care by improving diagnostic accuracy, treatment efficiency, and patient outcomes. Collaboration, transparency, and ongoing monitoring are essential for the successful integration of AI into clinical practice, with the ultimate goal of delivering more effective, efficient, and personalized cancer care. [ABSTRACT FROM AUTHOR]

As society moves towards a preventative approach to healthcare, there is growing interest in scientific research involving technology that can monitor and prevent adverse health outcomes. The primary objective of this paper is to develop an Internet of Things (IoT) wearable system based on Fried’s phenotype that is capable of detecting frailty. To determine user requirements, the system’s architecture was designed based on the findings of a questionnaire administered to individuals confirmed to be frail. A functional prototype was successfully developed and tested under real-world conditions. This paper introduces the methodology that was used to analyze the data collected from the prototype. It proposes an interdisciplinary approach to interpret wearable sensor data, providing a comprehensive overview through both visual representations and computational analyses facilitated by machine learning models. The findings of these analyses offer insights into the ways in which different types of activities can be classified and quantified as part of an overall physical activity level, which is recognized as an important indicator of frailty. The results provide the foundations for a new generation of affordable and non-intrusive systems able to detect and assess early signs of frailty.

This essay challenges the idea of progress as technological development in relation to medicine by focusing on people rather than things. It analyzes how the prevalence of such an idea of progress leads contemporary societies to a technofetishism that degrades community life and medical practice, contributing to the medicalization of social life. It is argued that the realization of technological potentialities depends on their forms of use, that the main motive of technological development is unlimited profit, and the priority developments are those that enhance social control which maintains the status quo. Intelligence as an intelligence quotient is criticized by proposing it as an attribute of the human being as a whole, manifested in the ways of thinking and acting of human beings in their circumstances, where affectivity and critical thinking are essential for their development; it is emphasized that its antecedent is the harmonic concert of planetary life, which contrasts with the prevailing human disharmony. It is proposed that artificial intelligence is the latest creation of technofetishism, which deposits vital attributes in technology, and that its use will accentuate the degradation of human and planetary life. Another idea of medical progress is proposed, based on forms of organization that is conducive to the development of inquisitive, critical, and collaborative skills that promote permanent improvement, whose distant horizon is dignified progress: the spiritual, intellectual, moral, and convivial sublimation of collectivities in harmony with the planetary ecosystem. [ABSTRACT FROM AUTHOR]

• Artificial intelligence (AI) models were generally effective for physical activity (PA) promotion (16 studies), outcome prediction (7 studies), and pattern recognition (1 study). • Twelve studies found AI-driven interventions, such as mobile apps, recommendation systems, and chatbots improved PA outcomes compared to traditional approaches. • An increasing trend was observed of adopting state-of-the-art deep learning and reinforcement learning models over standard machine learning. • Six key areas were identified for future AI adoption: personalized interventions, real-time monitoring and adaptation, multimodal data integration, evaluating effectiveness, expanding access, and preventing injuries. • Exploring emerging AI-driven strategies is essential for optimizing PA interventions and promoting public health. This scoping review aimed to offer researchers and practitioners an understanding of artificial intelligence (AI) applications in physical activity (PA) interventions; introduce them to prevalent machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms; and encourage the adoption of AI methodologies. A scoping review was performed in PubMed, Web of Science, Cochrane Library, and EBSCO focusing on AI applications for promoting PA or predicting related behavioral or health outcomes. AI methodologies were summarized and categorized to identify synergies, patterns, and trends informing future research. Additionally, a concise primer on predominant AI methodologies within the realm of PA was provided to bolster understanding and broader application. The review included 24 studies that met the predetermined eligibility criteria. AI models were found effective in detecting significant patterns of PA behavior and associations between specific factors and intervention outcomes. Most studies comparing AI models to traditional statistical approaches reported higher prediction accuracy for AI models on test data. Comparisons of different AI models yielded mixed results, likely due to model performance being highly dependent on the dataset and task. An increasing trend of adopting state-of-the-art DL and RL models over standard ML was observed, addressing complex human–machine communication, behavior modification, and decision-making tasks. Six key areas for future AI adoption in PA interventions emerged: personalized PA interventions, real-time monitoring and adaptation, integration of multimodal data sources, evaluation of intervention effectiveness, expanding access to PA interventions, and predicting and preventing injuries. The scoping review highlights the potential of AI methodologies for advancing PA interventions. As the field progresses, staying informed and exploring emerging AI-driven strategies is essential for achieving significant improvements in PA interventions and fostering overall well-being. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: The rapid advent of artificial intelligence (AI) and machine learning (ML) in the healthcare sector offers new horizons for patient education and has the potential to redefine patient-provider interactions. Our research aimed to gauge the efficacy of ChatGPT-4 in delivering accurate, current, and safe medical guidance on breast reconstruction, and benchmark its responses against other established patient information channels. Methods: We presented ChatGPT-4 with six frequently posed questions about breast reconstruction. The model's replies were critically assessed by a committee of experienced plastic and reconstructive surgeons. To ensure the precision of the information, its responses were further cross-referenced against two major medical databases. Results: The results revealed that ChatGPT-4 produced well-articulated, factually sound, and holistic answers to the presented inquiries. However, the platform showed constraints in offering tailored guidance and occasionally cited outdated or irrelevant references. Notably, the system consistently advocated for professional consultation for nuanced information. Conclusion: ChatGPT-4 has emerged as a potential supplementary resource in patient education concerning breast reconstruction. Nevertheless, to harness its full potential and ensure its seamless integration into healthcare, further refinements and advancements in AI tools are paramount. The study underscores the importance of continuous evaluation and enhancement for AI solutions in the evolving landscape of patient education. [ABSTRACT FROM AUTHOR]

Background/Aim: Overall survival (OS)-predictive models to clinically stratify patients with stage I Non-Small Cell Lung Cancer (NSCLC) undergoing stereotactic body radiation therapy (SBRT) are still unavailable. The aim of this work was to build a predictive model of OS in this setting. Patients and Methods: Clinical variables of patients treated in three Institutions with SBRT for stage I NSCLC were retrospectively collected into a reference cohort A (107 patients) and 2 comparative cohorts B1 (32 patients) and B2 (38 patients). A predictive model was built using Cox regression (CR) and artificial neural networks (ANN) on reference cohort A and then tested on comparative cohorts. Results: Cohort B1 patients were older and with worse chronic obstructive pulmonary disease (COPD) than cohort A. Cohort B2 patients were heavier smokers but had lower Charlson Comorbidity Index (CCI). At CR analysis for cohort A, only ECOG Performance Status 0-1 and absence of previous neoplasms correlated with better OS. The model was enhanced combining ANN and CR findings. The reference cohort was divided into prognostic Group 1 (0-2 score) and Group 2 (3-9 score) to assess model's predictions on OS: grouping was close to statistical significance (p=0.081). One and 2-year OS resulted higher for Group 1, lower for Group 2. In comparative cohorts, the model successfully predicted two groups of patients with divergent OS trends: higher for Group 1 and lower for Group 2. Conclusion: The produced model is a relevant tool to clinically stratify SBRT candidates into prognostic groups, even when applied to different cohorts. ANN are a valuable resource, providing useful data to build a prognostic model that deserves to be validated prospectively. [ABSTRACT FROM AUTHOR]

Significant developments are currently underway in the field of cancer research, particularly in Germany, regarding cancer registration and the use of medical information systems. The use of such systems contributes significantly to quality assurance and increased efficiency in data evaluation. The growing importance of artificial intelligence (AI) in cancer research is evident as these systems integrate AI for various purposes, i.e. to assist users in data analysis. This paper uses ensemble learning to classify the graphical user interface state of the medical information system CARESS. The results show that all ensemble learning models utilized achieved good performance. In particular, the gradient boosting algorithm performed the best with an accuracy of 97%. The results represent a starting point for further development of ensemble learning in medical data analysis, with the potential for integration into various applications such as recommender systems. [ABSTRACT FROM AUTHOR]

In recent years, with the development of artificial intelligence, deep learning has been gradually applied to clinical treatment and research. It has also found its way into the applications in radiotherapy, a crucial method for cancer treatment. This study summarizes the commonly used and latest deep learning algorithms (including transformer, and diffusion models), introduces the workflow of different radiotherapy, and illustrates the application of different algorithms in different radiotherapy modules, as well as the defects and challenges of deep learning in the field of radiotherapy, so as to provide some help for the development of automatic radiotherapy for cancer. [ABSTRACT FROM AUTHOR]