The translation of big data analytics and artificial intelligence (AI) into clinical decision support systems (CDSS) has advanced from proof-of-concept to real-world clinical practice. AI-informed CDSS show measurable improvements in diagnostic accuracy, risk stratification, resource utilization, and patient outcomes compared to traditional models, offering the potential to assist clinicians in managing symptom complexity and uncertainty in healthcare delivery. Despite this potential, access to large, high-quality, and granular data remains one of the most significant bottlenecks to AI-enabled CDSS. We argue that as healthcare systems increasingly adopt data-driven decision support, addressing the challenges of data accessibility and protection is essential to realizing the full potential of AI in clinical medicine. We use selected case examples of AI-informed CDSS in oncology, organ transplantation, diabetic retinopathy, epilepsy, spinal cord injury, rare disease, and emergency medicine to illustrate opportunities and challenges related to AI’s potential to improve patient outcomes. We discuss public/semi-public, provider-based/commercial, and government or national data sources that are currently available for the development of CDSS and we highlight the practical and ethical constraints associated with these data. We consider alternative data resources and ways that healthcare systems can strengthen data ecosystems to increase AI-driven CDSS efficacy and implementation to improve patient outcomes.
Daly et al. (2025) studied this question.