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[AI-enabled clinical decision support systems: challenges and opportunities].

Maximilian Tschochohei1,2, Lisa Christine Adams3, Keno Kyrill Bressem3,4

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AI-enabled clinical decision support systems (CDSS) enhance healthcare by providing evidence-based recommendations. Addressing challenges in data quality, integration, and trust is crucial for their responsible adoption.

Keywords:
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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Informatics
  • Health Data Science

Context:

  • Clinical decision-making is complex, time-sensitive, and error-prone.
  • AI-enabled Clinical Decision Support Systems (CDSS) leverage data for evidence-based recommendations.
  • Existing systems range from rule-based to AI-driven approaches.

Purpose:

  • To explore the capabilities and challenges of AI-CDSS in clinical practice.
  • To highlight the potential of AI-CDSS in improving diagnostic accuracy and patient outcomes.
  • To discuss the future role of LLMs and conversational AI in patient engagement and shared decision-making.

Summary:

  • AI-CDSS show promise in enhancing clinical decision-making, with successes noted in radiology and cardiology.
  • Key challenges include data quality, workflow integration, clinician trust, and ethical/legal considerations like data privacy.
  • Responsible development, including domain-specific data grounding, anonymization, and rigorous validation, is essential for safe and effective integration.

Impact:

  • AI-CDSS can improve diagnostic accuracy and patient outcomes in various medical fields.
  • Future AI-CDSS, potentially using LLMs, could enhance patient engagement and shared decision-making.
  • Thoughtful design and ethical oversight are critical for the safe and effective integration of AI into clinical workflows.