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TRUVACE RECORD VERSION
record: TRV-2026-0750
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-14T06:21:12.443080Z
status: published
lens: p_space
sector: health
headline: Enhancing diagnostic safety: addressing knowledge gaps for using human factors tools in the safe and effective use of AI - a proposed research agenda
dek: 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, psychometri…
gain_title: (none)
problem_title: Clinical AI deployment without human-factors tools risks automation bias, overreliance, fragmentation of care, and other unintended consequences that threaten diagnostic safety.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Clinical AI deployment without human-factors tools risks automation bias, overreliance, fragmentation of care, and other unintended consequences that threaten diagnostic safety.
problem_evidence: mitigates risks related to automation bias, overreliance, fragmentation of care, and unintended consequences
quick_read: On August 13, 2026, a peer-reviewed article in Diagnosis reported results from a two-day workshop of 18 interdisciplinary experts from three countries who sought to identify knowledge gaps in applying AI in clinical settings, using medical imaging as the primary use case.

The work matters because it shifts focus from model performance alone to human-factors integration, highlighting unresolved risks like automation bias and overreliance that could undermine diagnostic safety if AI is deployed without workflow redesign, team augmentation strategies, and balanced regulatory oversight.
limitation: Findings derive from a two-day expert consensus workshop with 18 participants rather than empirical evaluation of deployed AI systems, limiting generalizability to specific clinical workflows.
tag: Evidence-backed problem
key_points: Two-day workshop convened 18 interdisciplinary experts from three countries across academia, industry, health systems, and government. | Experts spanned quality and patient safety, human factors and systems engineering, radiology, nursing, informatics, psychology, psychometrics, and machine learning. | Consensus identified six major knowledge-gap domains including development, validation, integration and sustainability and redesign of existing healthcare systems.
rundown: The authors convened 18 experts from quality and safety, human factors and systems engineering, radiology and other specialties, nursing, informatics, cognitive psychology, psychometrics, and machine learning to examine medical imaging as a primary use case.

They framed six domains for future work: 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.

The agenda anticipates transformational AI capabilities in the next 5-7 years and calls for multidisciplinary collaboration to ensure AI supports diagnostic decision-making and integrates into clinical workflows while mitigating identified risks.
sources:
- peer_reviewed | Diagnosis | https://doi.org/10.1515/dx-2026-0082 | 2026-08-13
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