Artificial Intelligence in Clinical Decision Support:Current Applications, Challenges, and Future Directions
Keywords:
artificial intelligence, clinical decision support system, clinical evidence, data shift, medical AI governanceAbstract
Background: Artificial intelligence (AI) has become an important area of clinical decision support research, yet high performance in retrospective datasets does not establish patient benefit. Objective: To critically examine the applications, limitations, clinical evidence, and governance of AI-enabled clinical decision support systems (AI-CDSS). Approach: This focused narrative review synthesizes selected peer-reviewed studies, reporting standards, and regulatory guidance; it is not a systematic review. Findings: AI-CDSS can support imaging interpretation, risk stratification, clinical documentation, and monitoring. Prospective evidence suggests that improvements in care processes do not consistently translate into improvements in patient-important outcomes. Data shift, calibration, bias, automation effects, and workflow design remain major barriers. Conclusion: Deployment should be guided by intended-use-specific validation, prospective evaluation, human accountability, and ongoing surveillance. The primary benchmark should be demonstrable clinical value rather than algorithmic accuracy alone.
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