Inactive

December 18, 2024 - Amendment 01 posted. This amended provides a revised Attachment 1, which requests Key Personnel information on the cover page. All other documents remain unchanged. The rapid advan... December 18, 2024 - Amendment 01 posted. This amended provides a revised Attachment 1, which requests Key Personnel information on the cover page. All other documents remain unchanged. The rapid advancement of artificial intelligence (AI) technologies is transforming healthcare by improving efficiencies, reducing costs, and enhancing health outcomes. This potential is evident with over 850 FDA-approved medical devices now incorporating AI functionalities, a tenfold increase from 2018 to 2023. However, the ability to ensure the ongoing safety and efficacy of these AI systems has not kept pace. The conventional safety testing approach relies heavily on pre-market testing, assuming that these initial results will predict long-term performance. However, pre-market results often fail to account for variations in operational processes and patient demographics, leading to unpredictable post-market performance that currently requires manual oversight by vendors. Performance and Reliability Evaluation for Continuous modIfications and uSEability of AI (PRECISE-AI) aims to create a suite of self-correction techniques that make it possible to automatically maintain peak model performance of predictive AI components across diverse clinical settings. PRECISE-AI will advance novel approaches to optimally support clinician decision-making and scalably manage the performance of AI Decision Support Tools (AI-DSTs) after their commercial deployment. Key areas of innovation include continuous monitoring capabilities, degradation detection, root cause analysis, self-correction, and bidirectional communication with clinicians. This program will establish an open-source repository of tools to autonomously maintain the performance of clinical AI-DSTs while enhancing the interpretability and actionability of AI model outputs. The program will test these innovations in real-world settings to demonstrate measurable improvements in clinical decision-making. This program addresses the pressing need for continuous monitoring and updating of clinical AI models to ensure they remain effective and trustworthy over time.

Inactive

October 28, 2024: Revised DRAFT posted. Proposers Day slides added. September 5, 2024: Appendix A added This is a DRAFT program solicitation. A final solicitation will be posted at a later date. ARPA-... October 28, 2024: Revised DRAFT posted. Proposers Day slides added. September 5, 2024: Appendix A added This is a DRAFT program solicitation. A final solicitation will be posted at a later date. ARPA-H intends to hold a Proposers' Day; details will be provided as they become available. Interested parties are encouraged to submit questions on the contents of this draft solicitation. No awards will be made as a result of this posting. The rapid advancement of artificial intelligence (AI) technologies is transforming healthcare by improving efficiencies, reducing costs, and enhancing health outcomes. This potential is evident with over 850 FDA-approved medical devices now incorporating AI functionalities, a tenfold increase from 2018 to 2023. However, the ability to ensure the ongoing safety and efficacy of these AI systems has not kept pace. The conventional safety testing approach relies heavily on pre-market testing, assuming that these initial results will predict long-term performance. However, pre-market results often fail to account for variations in operational processes and patient demographics, leading to unpredictable post-market performance that currently requires manual oversight by vendors. Performance and Reliability Evaluation for Continuous modIfications and uSEability of AI (PRECISE-AI) aims to create a suite of self-correction techniques that make it possible to automatically maintain peak model performance of predictive AI components across diverse clinical settings. PRECISE-AI will advance novel approaches to optimally support clinician decision-making and scalably manage the performance of AI Decision Support Tools (AI-DSTs) after their commercial deployment. Key areas of innovation include continuous monitoring capabilities, degradation detection, root cause analysis, self-correction, and bidirectional communication with clinicians. This program will establish an open-source repository of tools to autonomously maintain the performance of clinical AI-DSTs while enhancing the interpretability and actionability of AI model outputs. The program will test these innovations in real-world settings to demonstrate measurable improvements in clinical decision-making. This program addresses the pressing need for continuous monitoring and updating of clinical AI models to ensure they remain effective and trustworthy over time.