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/https://libri-media.knygos-static.lt/916d237b-3a09-4ea4-be3f-e019108b45f7/1)
- Leidėjas: Taylor & Francis Ltd
- Metai: 2026
- Puslapiai: 240
- ISBN-10: 1041249616
- ISBN-13: 9781041249610
- Formatas: 15.6 x 23.4 x cm, kieti viršeliai
- Kalba: Anglų
AI Safety in Healthcare Systems (el. knyga) (skaityta knyga) | knygos.lt
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Aprašymas
The book explores real-world applications of explainable AI (XAI) safely in healthcare, focusing on disease diagnosis, treatment planning, and patient management, while demonstrating how XAI enhances clinical decision-making and patient outcomes. It discusses the integration of explainable large language models (LLMs) into electronic health records (EHRs) and clinical workflows to facilitate data analysis, improve documentation, and support care delivery. Challenges such as privacy concerns, data complexity, and adapting models to specific domains are addressed, alongside evaluation techniques for explainability, including metrics, benchmarks, and human-centered assessments to ensure AI explanations are accurate and clinically relevant. Ethical considerations like fairness, accountability, and balancing transparency with patient confidentiality are highlighted, with case studies and empirical evidence illustrating the benefits and challenges of safely implementing XAI in clinical settings.
- Leidėjas: Taylor & Francis Ltd
- Metai: 2026
- Puslapiai: 240
- ISBN-10: 1041249616
- ISBN-13: 9781041249610
- Formatas: 15.6 x 23.4 x cm, kieti viršeliai
- Kalba: Anglų
The book explores real-world applications of explainable AI (XAI) safely in healthcare, focusing on disease diagnosis, treatment planning, and patient management, while demonstrating how XAI enhances clinical decision-making and patient outcomes. It discusses the integration of explainable large language models (LLMs) into electronic health records (EHRs) and clinical workflows to facilitate data analysis, improve documentation, and support care delivery. Challenges such as privacy concerns, data complexity, and adapting models to specific domains are addressed, alongside evaluation techniques for explainability, including metrics, benchmarks, and human-centered assessments to ensure AI explanations are accurate and clinically relevant. Ethical considerations like fairness, accountability, and balancing transparency with patient confidentiality are highlighted, with case studies and empirical evidence illustrating the benefits and challenges of safely implementing XAI in clinical settings.
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