Beyond Black-Box Algorithms: Artificial Intelligence for Enhancing Diagnostic Tactics in Clinical Care
Keywords:
artificial intelligence, clinical decision support, deep learning, diagnostic accuracy, medical imaging, electronic health records, explainability, bias, implementationAbstract
Artificial intelligence (AI) is reshaping diagnostic strategy by augmenting clinical reasoning, improving pattern recognition, and enabling data‑driven decision support across specialties. AI models in imaging, pathology, and electronic health records (EHR) analysis already achieve diagnostic performance comparable to, or exceeding, human experts in selected tasks, while reducing variability and workload., Yet, most systems remain poorly explainable, vulnerable to bias, and insufficiently validated in real-world primary care and emergency settings, limiting safe implementation., This article synthesizes recent evidence on AI‑enabled diagnostic support, outlines methodological building blocks, and compares core technical approaches that underpin modern systems. It proposes a pragmatic, clinician‑oriented framework for integrating AI into diagnostic pathways—triage, hypothesis generation, risk stratification, and feedback—while emphasizing governance, explainability, and equity.
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