Artículos relacionados a Medical LLMs for Clinical Safety Assessment
Sinopsis
Large Language Models (LLMs) are increasingly being explored in clinical settings, where they promise to support tasks such as documentation, decision support, and patient communication. However, the integration of medical LLMs introduces significant clinical safety considerations, as errors, or inappropriate recommendations can have real-world consequences for patient care. Clinical safety assessment plays a critical role in evaluating whether these systems perform reliably and within acceptable risk boundaries across diverse clinical scenarios. This assessment requires different methods to test accuracy, bias, and failure modes, as well as alignment with clinical standards, regulatory expectations, and ethical principles, to ensure that medical LLMs augment. Medical LLMs for Clinical Safety Assessment provides a comprehensive overview of clinical safety evaluation frameworks for medical LLMs. It connects AI research with medical practice by providing evidence-based models. Covering topics such as LLMS, medical technologies, and clinical assessment, this book is an excellent resource for researchers, machine learning engineers, data scientists, health leaders, policy makers, academicians, and graduate students.
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Acerca de los autores
Fernando Ortiz-Rodriguez is a Full Professor and Head of the Artificial Intelligence and Innovation Lab at Tamaulipas Autonomous University. He was the Executive Director at the International Institute of Studies (IIES). He created the First Business School in Tamaulipas, Mexico. He worked as the Information Technology Manager at Emerson Electric, where he developed more than 40 pieces of software, some of them used globally in Emerson, and he achieved technology convergence by implementing the first efforts on IoT and Industry 4.0. Fernando is a member of National Systems Researchers (SNI) Level 1 of the National Council of Science and Technology (CONACYT) and INDEX IT Advisor
Mrignainy Kansal is a Ph.D. researcher in Computer Science and Engineering at Netaji Subhas University of Technology (NSUT), New Delhi, India. She previously served as an Assistant Professor in the Department of Information Technology at Ajay Kumar Garg sEngineering College (AKGEC), Ghaziabad, where she contributed to teaching, research, and academic development. She brings over six years of academic experience in higher education. Her research lies at the intersection of software engineering and artificial intelligence, with a primary focus on software defect prediction, explainable AI, and intelligent data-driven systems. She has an extensive publication record, with contributions in reputed journals and conferences including Springer, IEEE, Wiley, and IGI Global. Her work emphasizes the development of robust, interpretable, and scalable solutions for real-world software quality challenges. She has also made notable contributions through edited volumes, book chapters, and active participation in international conferences, including serving as a Conference Program Committee Member. She is the recipient of multiple Best Paper Awards and holds several published and granted patents in emerging domains such as artificial intelligence, blockchain, and smart systems. She is an IEEE member and remains actively engaged in advancing research, innovation, and scholarly collaboration in intelligent software engineering.
Rajesh Kumar Dhanaraj is a distinguished Professor at Symbiosis International (Deemed University) in Pune, India. Prior to this, he served as a Professor at the School of Computing Science & Engineering at Galgotias University in Greater Noida, India. His exceptional academic and research contributions have placed him among the top 2% of scientists globally, an honor recognized by Elsevier and Stanford University. Dr. Dhanaraj completed his B.E. in Computer Science and Engineering from Anna University Chennai, followed by an M.Tech from Anna University Coimbatore. He earned his Ph.D. in Computer Science from Anna University, Chennai. His prolific career includes authoring and editing over 100 books on advanced technologies and holding 27 patents. He has published over 200 articles in esteemed journals and international conferences, including six papers in IEEE Transactions. As a mentor, Dr. Dhanaraj has guided four PhD candidates to completion, with eight more currently under his supervision. He is renowned for delivering insightful tech talks on disruptive technologies and has established valuable collaborations with professors from top QS-ranked universities globally. Dr. Dhanaraj’s research interests include Applied AI, Cyber-Physical Systems, and Wireless Sensor Networks. His expertise in these areas has led to numerous research talks at esteemed institutions. He is a Senior Member of the IEEE, and a member of the CSTA and IAENG. Additionally, he serves as an Associate Editor and Guest Editor for several prestigious journals and is an Expert Advisory Panel Member of Texas Instruments Inc., USA.
Firoz Khan is an academic and researcher with a distinguished Ph.D. in Computer Science from the British University in Dubai. He also holds a Master’s in Information Technology from the University of Southern Queensland (USQ) and a Master’s in Information, Network, and Computer Security from the New York Institute of Technology (NYIT). His research interests include IoT applications in agriculture, automotive cybersecurity, and the integration of AI with cybersecurity in healthcare. He is also exploring advanced topics such as precision farming techniques, privacy-preserving models, and liquid neural networks for time-series prediction in healthcare. As an educator, Firoz specializes in teaching IoT security, cybersecurity, and advanced networking. He emphasizes a hands-on, active learning approach to equip students with practical skills and foster innovation, preparing them for challenges in an ever-evolving technological landscape.
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Paperback. Condición: new. Paperback. Large Language Models (LLMs) are increasingly being explored in clinical settings, where they promise to support tasks such as documentation, decision support, and patient communication. However, the integration of medical LLMs introduces significant clinical safety considerations, as errors, or inappropriate recommendations can have real-world consequences for patient care. Clinical safety assessment plays a critical role in evaluating whether these systems perform reliably and within acceptable risk boundaries across diverse clinical scenarios. This assessment requires different methods to test accuracy, bias, and failure modes, as well as alignment with clinical standards, regulatory expectations, and ethical principles, to ensure that medical LLMs augment. Medical LLMs for Clinical Safety Assessment provides a comprehensive overview of clinical safety evaluation frameworks for medical LLMs. It connects AI research with medical practice by providing evidence-based models. Covering topics such as LLMS, medical technologies, and clinical assessment, this book is an excellent resource for researchers, machine learning engineers, data scientists, health leaders, policy makers, academicians, and graduate students. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9798337378633
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Large Language Models (LLMs) are increasingly being explored in clinical settings, where they promise to support tasks such as documentation, decision support, and patient communication. However, the integration of medical LLMs introduces significant clinical safety considerations, as errors, or inappropriate recommendations can have real-world consequences for patient care. Clinical safety assessment plays a critical role in evaluating whether these systems perform reliably and within acceptable risk boundaries across diverse clinical scenarios. This assessment requires different methods to test accuracy, bias, and failure modes, as well as alignment with clinical standards, regulatory expectations, and ethical principles, to ensure that medical LLMs augment. Medical LLMs for Clinical Safety Assessment provides a comprehensive overview of clinical safety evaluation frameworks for medical LLMs. It connects AI research with medical practice by providing evidence-based models. Covering topics such as LLMS, medical technologies, and clinical assessment, this book is an excellent resource for researchers, machine learning engineers, data scientists, health leaders, policy makers, academicians, and graduate students. Nº de ref. del artículo: 9798337378633
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Taschenbuch. Condición: Neu. Medical LLMs for Clinical Safety Assessment | Fernando Ortiz-Rodriguez (u. a.) | Taschenbuch | Englisch | 2026 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798337378633 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Nº de ref. del artículo: 135708661
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