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Amin M, Martínez-Heras E, Ontaneda D, Prados Carrasco F. Artificial Intelligence and Multiple Sclerosis. Curr Neurol Neurosci Rep 2024; 24:233-243. [PMID: 38940994 PMCID: PMC11258192 DOI: 10.1007/s11910-024-01354-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/18/2024] [Indexed: 06/29/2024]
Abstract
In this paper, we analyse the different advances in artificial intelligence (AI) approaches in multiple sclerosis (MS). AI applications in MS range across investigation of disease pathogenesis, diagnosis, treatment, and prognosis. A subset of AI, Machine learning (ML) models analyse various data sources, including magnetic resonance imaging (MRI), genetic, and clinical data, to distinguish MS from other conditions, predict disease progression, and personalize treatment strategies. Additionally, AI models have been extensively applied to lesion segmentation, identification of biomarkers, and prediction of outcomes, disease monitoring, and management. Despite the big promises of AI solutions, model interpretability and transparency remain critical for gaining clinician and patient trust in these methods. The future of AI in MS holds potential for open data initiatives that could feed ML models and increasing generalizability, the implementation of federated learning solutions for training the models addressing data sharing issues, and generative AI approaches to address challenges in model interpretability, and transparency. In conclusion, AI presents an opportunity to advance our understanding and management of MS. AI promises to aid clinicians in MS diagnosis and prognosis improving patient outcomes and quality of life, however ensuring the interpretability and transparency of AI-generated results is going to be key for facilitating the integration of AI into clinical practice.
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Affiliation(s)
- Moein Amin
- Mellen Center for Multiple Sclerosis Treatment and Research, Cleveland Clinic, Cleveland, OH, USA
| | - Eloy Martínez-Heras
- Neuroimmunology and Multiple Sclerosis Unit, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Daniel Ontaneda
- Mellen Center for Multiple Sclerosis Treatment and Research, Cleveland Clinic, Cleveland, OH, USA
| | - Ferran Prados Carrasco
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain.
- Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.
- Center for Medical Image Computing, University College London, London, UK.
- National Institute for Health Research Biomedical Research Centre at UCL and UCLH, London, UK.
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Zhui L, Fenghe L, Xuehu W, Qining F, Wei R. Ethical Considerations and Fundamental Principles of Large Language Models in Medical Education: Viewpoint. J Med Internet Res 2024; 26:e60083. [PMID: 38971715 PMCID: PMC11327620 DOI: 10.2196/60083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 07/06/2024] [Indexed: 07/08/2024] Open
Abstract
This viewpoint article first explores the ethical challenges associated with the future application of large language models (LLMs) in the context of medical education. These challenges include not only ethical concerns related to the development of LLMs, such as artificial intelligence (AI) hallucinations, information bias, privacy and data risks, and deficiencies in terms of transparency and interpretability but also issues concerning the application of LLMs, including deficiencies in emotional intelligence, educational inequities, problems with academic integrity, and questions of responsibility and copyright ownership. This paper then analyzes existing AI-related legal and ethical frameworks and highlights their limitations with regard to the application of LLMs in the context of medical education. To ensure that LLMs are integrated in a responsible and safe manner, the authors recommend the development of a unified ethical framework that is specifically tailored for LLMs in this field. This framework should be based on 8 fundamental principles: quality control and supervision mechanisms; privacy and data protection; transparency and interpretability; fairness and equal treatment; academic integrity and moral norms; accountability and traceability; protection and respect for intellectual property; and the promotion of educational research and innovation. The authors further discuss specific measures that can be taken to implement these principles, thereby laying a solid foundation for the development of a comprehensive and actionable ethical framework. Such a unified ethical framework based on these 8 fundamental principles can provide clear guidance and support for the application of LLMs in the context of medical education. This approach can help establish a balance between technological advancement and ethical safeguards, thereby ensuring that medical education can progress without compromising the principles of fairness, justice, or patient safety and establishing a more equitable, safer, and more efficient environment for medical education.
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Affiliation(s)
- Li Zhui
- Department of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Li Fenghe
- Department of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wang Xuehu
- Department of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fu Qining
- Department of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ren Wei
- Department of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Bragazzi NL, Garbarino S. Toward Clinical Generative AI: Conceptual Framework. JMIR AI 2024; 3:e55957. [PMID: 38875592 PMCID: PMC11193080 DOI: 10.2196/55957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 04/08/2024] [Accepted: 05/06/2024] [Indexed: 06/16/2024]
Abstract
Clinical decision-making is a crucial aspect of health care, involving the balanced integration of scientific evidence, clinical judgment, ethical considerations, and patient involvement. This process is dynamic and multifaceted, relying on clinicians' knowledge, experience, and intuitive understanding to achieve optimal patient outcomes through informed, evidence-based choices. The advent of generative artificial intelligence (AI) presents a revolutionary opportunity in clinical decision-making. AI's advanced data analysis and pattern recognition capabilities can significantly enhance the diagnosis and treatment of diseases, processing vast medical data to identify patterns, tailor treatments, predict disease progression, and aid in proactive patient management. However, the incorporation of AI into clinical decision-making raises concerns regarding the reliability and accuracy of AI-generated insights. To address these concerns, 11 "verification paradigms" are proposed in this paper, with each paradigm being a unique method to verify the evidence-based nature of AI in clinical decision-making. This paper also frames the concept of "clinically explainable, fair, and responsible, clinician-, expert-, and patient-in-the-loop AI." This model focuses on ensuring AI's comprehensibility, collaborative nature, and ethical grounding, advocating for AI to serve as an augmentative tool, with its decision-making processes being transparent and understandable to clinicians and patients. The integration of AI should enhance, not replace, the clinician's judgment and should involve continuous learning and adaptation based on real-world outcomes and ethical and legal compliance. In conclusion, while generative AI holds immense promise in enhancing clinical decision-making, it is essential to ensure that it produces evidence-based, reliable, and impactful knowledge. Using the outlined paradigms and approaches can help the medical and patient communities harness AI's potential while maintaining high patient care standards.
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Affiliation(s)
- Nicola Luigi Bragazzi
- Human Nutrition Unit, Department of Food and Drugs, University of Parma, Parma, Italy
| | - Sergio Garbarino
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal/Child Sciences, University of Genoa, Genoa, Italy
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Burnette H, Pabani A, von Itzstein MS, Switzer B, Fan R, Ye F, Puzanov I, Naidoo J, Ascierto PA, Gerber DE, Ernstoff MS, Johnson DB. Use of artificial intelligence chatbots in clinical management of immune-related adverse events. J Immunother Cancer 2024; 12:e008599. [PMID: 38816231 PMCID: PMC11141185 DOI: 10.1136/jitc-2023-008599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/14/2024] [Indexed: 06/01/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) chatbots have become a major source of general and medical information, though their accuracy and completeness are still being assessed. Their utility to answer questions surrounding immune-related adverse events (irAEs), common and potentially dangerous toxicities from cancer immunotherapy, are not well defined. METHODS We developed 50 distinct questions with answers in available guidelines surrounding 10 irAE categories and queried two AI chatbots (ChatGPT and Bard), along with an additional 20 patient-specific scenarios. Experts in irAE management scored answers for accuracy and completion using a Likert scale ranging from 1 (least accurate/complete) to 4 (most accurate/complete). Answers across categories and across engines were compared. RESULTS Overall, both engines scored highly for accuracy (mean scores for ChatGPT and Bard were 3.87 vs 3.5, p<0.01) and completeness (3.83 vs 3.46, p<0.01). Scores of 1-2 (completely or mostly inaccurate or incomplete) were particularly rare for ChatGPT (6/800 answer-ratings, 0.75%). Of the 50 questions, all eight physician raters gave ChatGPT a rating of 4 (fully accurate or complete) for 22 questions (for accuracy) and 16 questions (for completeness). In the 20 patient scenarios, the average accuracy score was 3.725 (median 4) and the average completeness was 3.61 (median 4). CONCLUSIONS AI chatbots provided largely accurate and complete information regarding irAEs, and wildly inaccurate information ("hallucinations") was uncommon. However, until accuracy and completeness increases further, appropriate guidelines remain the gold standard to follow.
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Affiliation(s)
- Hannah Burnette
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Aliyah Pabani
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Mitchell S von Itzstein
- Harold C Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Benjamin Switzer
- Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Run Fan
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Fei Ye
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Igor Puzanov
- Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | | | - Paolo A Ascierto
- Department of Melanoma, Cancer Immunotherapy and Development Therapeutics, Istituto Nazionale Tumori IRCCS Fondazione Pascale, Napoli, Campania, Italy
| | - David E Gerber
- Harold C Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Marc S Ernstoff
- ImmunoOncology Branch (IOB), Developmental Therapeutics Program, Cancer Therapy and Diagnosis Division, National Cancer Institute (NCI), National Institutes of Health, Bethesda, Maryland, USA
| | - Douglas B Johnson
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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