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Diagnostic scope: the AI can't see what the mind doesn't know.
Gary E Weissman1,2,3,4, Laura Zwaan5, Sigall K Bell6
114640 Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA, USA.
Diagnosis (Berlin, Germany)
|December 3, 2024
Summary
Understanding diagnostic scope is crucial for developing effective artificial intelligence (AI) diagnostic tools. Accurately measuring this scope ensures AI systems align with clinical needs, promoting diagnostic excellence and patient safety.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Medicine
- Clinical Decision Support
Background:
- Diagnostic scope, the range of diagnoses in a clinical setting, is vital for AI training.
- Its impact on AI systems and the diagnostic process is currently under-explored.
Purpose of the Study:
- Define diagnostic scope and its role in AI-based diagnostic decision support systems.
- Review challenges in measuring and using diagnostic scope.
- Identify knowledge gaps for future research.
Main Methods:
- Conceptual definition of diagnostic scope.
- Discussion of its role in AI development.
- Review of existing literature and challenges.
Main Results:
- Diagnostic scope differs from differential diagnosis, varying by clinical setting, population, and resources.
- Divergence between true, observed, and considered scope presents challenges and opportunities for AI.
- Tailoring AI tools requires specifying and measuring diagnostic scope for specific settings.
Conclusions:
- AI tools enhance diagnostic excellence when aligned with patient/clinician needs and trained on accurate diagnostic scope.
- Understanding and evaluating diagnostic scope is key for optimal human-AI collaboration in diagnostics.
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