Evaluating AI, Apps, and Digital Solutions-View Presentation Slides

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The document is a comprehensive overview of evaluating artificial intelligence (AI), apps, and digital solutions in healthcare, particularly focusing on mental health. It highlights the importance of assessing digital tools critically before recommending them to patients. The American Psychiatric Association's App Evaluation Model is a key feature of this approach, emphasizing criteria such as privacy, safety, efficacy, ease of use, and data sharing.<br /><br />Rising regulatory challenges with large language models (LLMs) are discussed, focusing on patient data privacy, intellectual property, medical malpractice, and quality control. The document details the need for transparency, informed consent, and fair content generated by AI systems to mitigate inherent biases and ensure patient safety and reliability.<br /><br />Three epochs of AI in healthcare are mentioned, highlighting the progression from basic machine learning applications to more complex and integrated AI solutions like Generative AI, which assist in clinical decision-making and administrative functions. The document conveys concerns about AI-generated errors and biases—such as "hallucinations", variability, sycophancy, and the challenges in "complete-the-narrative" contexts within healthcare settings.<br /><br />The text discusses various healthcare coalitions and initiatives formed to enhance trustworthy AI deployment, including the Coalition for Health AI (CHAI), Trustworthy & Responsible AI Network (TRAIN), VALID AI, and collaborations by institutions such as Duke Health with Microsoft. These initiatives aim to create standards, guidelines, and collaborations to ensure the ethical use of AI in healthcare.<br /><br />Key considerations in cybersecurity, liability, privacy, and transparency for AI use in healthcare are highlighted, aligning with the American Medical Association's new principles for AI development and use. The summary emphasizes the significance of continued education, advocacy for AI best practices, and responsible implementation to prevent potential harms associated with AI advancements.

Keywords

AI in healthcare

mental health

App Evaluation Model

privacy and safety

large language models

patient data privacy

Generative AI

AI biases

trustworthy AI

cybersecurity