TY - JOUR
T1 - Toward a responsible future
T2 - recommendations for AI-enabled clinical decision support
AU - Labkoff, Steven
AU - Oladimeji, Bilikis
AU - Kannry, Joseph
AU - Solomonides, Anthony
AU - Leftwich, Russell
AU - Koski, Eileen
AU - Joseph, Amanda L.
AU - Lopez-Gonzalez, Monica
AU - Fleisher, Lee A.
AU - Nolen, Kimberly
AU - Dutta, Sayon
AU - Levy, Deborah R.
AU - Price, Amy
AU - Barr, Paul J.
AU - Hron, Jonathan D.
AU - Lin, Baihan
AU - Srivastava, Gyana
AU - Pastor, Nuria
AU - Luque, Unai Sanchez
AU - Bui, Tien Thi Thuy
AU - Singh, Reva
AU - Williams, Tayler
AU - Weiner, Mark G.
AU - Naumann, Tristan
AU - Sittig, Dean F.
AU - Jackson, Gretchen Purcell
AU - Quintana, Yuri
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/11/1
Y1 - 2024/11/1
N2 - Background: Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging. Objectives: This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients. Materials and Methods: In May 2023, the Division of Clinical Informatics at Beth Israel Deaconess Medical Center and the American Medical Informatics Association co-sponsored a working group on AI in healthcare. In August 2023, there were 4 webinars on AI topics and a 2-day workshop in September 2023 for consensus-building. The event included over 200 industry stakeholders, including clinicians, software developers, academics, ethicists, attorneys, government policy experts, scientists, and patients. The goal was to identify challenges associated with the trusted use of AI-enabled CDS in medical practice. Key issues were identified, and solutions were proposed through qualitative analysis and a 4-month iterative consensus process. Results: Our work culminated in several key recommendations: (1) building safe and trustworthy systems; (2) developing validation, verification, and certification processes for AI-CDS systems; (3) providing a means of safety monitoring and reporting at the national level; and (4) ensuring that appropriate documentation and end-user training are provided.
AB - Background: Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging. Objectives: This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients. Materials and Methods: In May 2023, the Division of Clinical Informatics at Beth Israel Deaconess Medical Center and the American Medical Informatics Association co-sponsored a working group on AI in healthcare. In August 2023, there were 4 webinars on AI topics and a 2-day workshop in September 2023 for consensus-building. The event included over 200 industry stakeholders, including clinicians, software developers, academics, ethicists, attorneys, government policy experts, scientists, and patients. The goal was to identify challenges associated with the trusted use of AI-enabled CDS in medical practice. Key issues were identified, and solutions were proposed through qualitative analysis and a 4-month iterative consensus process. Results: Our work culminated in several key recommendations: (1) building safe and trustworthy systems; (2) developing validation, verification, and certification processes for AI-CDS systems; (3) providing a means of safety monitoring and reporting at the national level; and (4) ensuring that appropriate documentation and end-user training are provided.
UR - https://www.scopus.com/pages/publications/85206956216
U2 - 10.1093/jamia/ocae209
DO - 10.1093/jamia/ocae209
M3 - Article
C2 - 39325508
AN - SCOPUS:85206956216
SN - 1067-5027
VL - 31
SP - 2730
EP - 2739
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - 11
ER -