Enhancing diagnostic safety: addressing knowledge gaps for using human factors tools in the safe and effective use of AI – a proposed research agenda

INTRODUCTION: Identify knowledge gaps in applying artificial intelligence in clinical settings, using medical imaging as a primary use case to enhance diagnostic efficacy, efficiency, and patient and provider safety. METHODS: We convened a two-day workshop with 18 interdisciplinary experts from three countries. Experts represented quality and patient safety, human factors and systems engineering, radiology and other medical specialties, nursing, medical informatics, cognitive and perceptual psychology, psychometrics and statistics, and machine learning, drawn from academia, industry, health systems, and government. RESULTS: We identified by consensus six major knowledge-gap domains, with specific research questions for each domain: development, validation, integration and sustainability; redesign of existing healthcare systems; human and team augmentation; deployment of adaptive-learning "Foundation Models;" and balancing innovation, standardization, and regulatory oversight. CONCLUSIONS: We recommend employing a multidisciplinary collaborative approach in future research to leverage transformational AI capabilities anticipated in the next 5-7 years for each of the identified knowledge-gap domains, including ensuring that clinical AI supports diagnostic decision-making, integrates into clinical workflows, and mitigates risks related to automation bias, overreliance, fragmentation of care, and unintended consequences.

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Publication Details

Journal
Diagnosis
Published
2026-08-12
DOI
https://doi.org/10.1515/dx-2026-0082
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00

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article

Enhancing diagnostic safety: addressing knowledge gaps for using human factors tools in the safe and effective use of AI – a proposed research agenda

Michael H. Kanter, Linda Gangai, Timothy J. Mosher, Emily S. Patterson et al.
Diagnosis
Artificial Intelligence in Healthcare and Education
article

Enhancing diagnostic safety: addressing knowledge gaps for using human factors tools in the safe and effective use of AI – a proposed research agenda

Michael H. Kanter, Linda Gangai, Timothy J. Mosher, Emily S. Patterson, Ken Catchpole, Michael A. Bruno, Charlotte van Sassen, Melissa Carraway, Charles Η. Kahn, Jeff Brady, Mark Graber, Grayson L. Baird, Mary D. Patterson, Soundar Kumara, David Bates, Ayse P. Gurses, Kristen E. Miller, Susan Shelmerdine, Elizabeth A. Krupinski, Sarah E. Parker, Chris Mamrol, Joshua Streit
article en

Abstract

INTRODUCTION: Identify knowledge gaps in applying artificial intelligence in clinical settings, using medical imaging as a primary use case to enhance diagnostic efficacy, efficiency, and patient and provider safety. METHODS: We convened a two-day workshop with 18 interdisciplinary experts from three countries. Experts represented quality and patient safety, human factors and systems engineering, radiology and other medical specialties, nursing, medical informatics, cognitive and perceptual psychology, psychometrics and statistics, and machine learning, drawn from academia, industry, health systems, and government. RESULTS: We identified by consensus six major knowledge-gap domains, with specific research questions for each domain: development, validation, integration and sustainability; redesign of existing healthcare systems; human and team augmentation; deployment of adaptive-learning "Foundation Models;" and balancing innovation, standardization, and regulatory oversight. CONCLUSIONS: We recommend employing a multidisciplinary collaborative approach in future research to leverage transformational AI capabilities anticipated in the next 5-7 years for each of the identified knowledge-gap domains, including ensuring that clinical AI supports diagnostic decision-making, integrates into clinical workflows, and mitigates risks related to automation bias, overreliance, fragmentation of care, and unintended consequences.

Diagnosis
University of Maryland, Baltimore (US), Pennsylvania State University (US), Kaiser Permanente (US), Harvard University (US), Johns Hopkins University (US), Emory University (US), Medical University of South Carolina (US), Amherst College (US), Georgetown University (US), University of Massachusetts Amherst (US), Brown University (US), Johns Hopkins Medicine (US), Great Ormond Street Hospital (GB), Harvard University Press (US), Erasmus MC (NL), University of Florida (US), University of Massachusetts Boston (US), Erasmus MC Cancer Institute (NL), Adventist HealthCare Rehabilitation (US), Wissenschaftliches Institut der AOK (DE), University College London (GB), Carilion Clinic (US), Stony Brook University (US), University of Pennsylvania (US), Penn State Milton S. Hershey Medical Center (US), MedStar Health (US), National Patient Safety Foundation (US), Erasmus University Rotterdam (NL)
Agency for Healthcare Research and Quality
Industry, innovation and infrastructure
Openalex Percentile: Top 13%
Artificial Intelligence in Healthcare and Education
DOIOpen Access PDFOpenAlex
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