Titlebook: Artificial Intelligence in Brain and Mental Health: Philosophical, Ethical & Policy Issues; Fabrice Jotterand,Marcello Ienca Book 2021 The

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Big Data in Medical AI: How Larger Data Sets Lead to Robust, Automated Learning for Medicineectly from the raw data. Such systems are state-of-the-art in complex learning problems with rich, high-dimensional data sets—such as speech or image recognition. All of these advanced approaches are predicated on access to massive data sets, which presents unique challenges for developing AI system

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Machine Learning in Stroke Medicine: Opportunities and Challenges for Risk Prediction and Prevention identifies important technological and methodological challenges for using ML in these contexts. The chapter concludes by drawing the readers’ attention to some of the questions and ethical challenges that arise as clinicians widely adopt ML-based applications in practice.

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Minding the AI: Ethical Challenges and Practice for AI Mental Health Care Toolsaddressing existing inequities in mental health care. Protection of patients and other users of mental health AI tools from potential misuse of their health information and potential negative repercussions from sharing their data is another critical area of concern. Finally, AI tools present challen

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Fallsammlung zum Privatversicherungsrecht introduced during development), and the black-box problem (lack of transparency regarding the agent’s behavioral choices) are also discussed, along with recommendations to begin to address these issues.

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