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Clinical artificial intelligence (AI) applications have the potential to radically transform healthcare in diverse areas of impact including precision medicine, clinical decision support tools, virtual therapeutic agents, and predictive diagnostics. Designing, integrating, evaluating, and sustaining clinical ...

Clinical artificial intelligence (AI) applications have the potential to radically transform healthcare in diverse areas of impact including precision medicine, clinical decision support tools, virtual therapeutic agents, and predictive diagnostics. Designing, integrating, evaluating, and sustaining clinical AI tools effectively presents significant challenges in terms of clinical validation, market regulation, clinician acceptance, and equitable benefits for patients, especially when considered in the context of complex health care ecosystems. Here, human-centered design approaches elucidate the inter-related needs and experiences of diverse stakeholders engaged in combining data, analytics, and cultures of care in continuously evolving cycles of health improvement.

The goal of this Research Topic is to understand the implications for designing and integrating clinical AI into healthcare settings from approaches that aim for human-centered AI. We are particularly curious about process challenges for implementation and gaps in sustainability for new AI being implemented. Target stakeholders include those that are designing and building AI technologies to address clinical challenges and consideration of how to help them better understand the difficulties in actual implementation in clinical settings. We seek to highlight the value of an empathic human-centered approach, which can facilitate better design, more appropriate implementation, and the likelihood of maintaining longer-term sustainable solutions.

Topics covered may include:
• Design research to inform Clinical AI (methods and case studies)
• Use of Implementation Science in Clinical AI practice integration
• Metrics for and measures of Clinical AI uptake, utility, and impact on users
• Case studies of Clinical AI Implementation
• Novel data sets and data capture methods to inform Clinical AI
• Empirical studies examining clinician and patient perceptions and risk-beliefs of Clinical AI
• Studies of legislative, professional, and regulatory frameworks for Clinical AI
• Medicolegal and ethical requirements and moral and professional responsibilities in an era of Clinical AI

Keywords: Human-Centered Design, AI, Clinical Applications, Implementation, Evaluation, Metrics, Human-AI Interaction


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