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Despite this potential, access to large amounts of high-quality and granular data remains one of the most significant bottlenecks to AI-enabled CDSSs. We argue that as health care 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 CDSSs in oncology, organ transplantation, diabetic retinopathy, epilepsy, spinal cord injury, rare disease diagnosis, and emergency medicine to illustrate opportunities and challenges related to AI\u2019s potential to improve patient outcomes. We discuss public and semipublic, medical institutional and commercial, and government and national data sources that are currently available for the development of CDSSs and highlight the practical and ethical constraints associated with these data. We consider alternative data resources and ways in which health care systems can strengthen data ecosystems to increase AI-driven CDSS efficacy and implementation to improve patient outcomes.<\/jats:p>","DOI":"10.2196\/71532","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T07:40:07Z","timestamp":1770277207000},"page":"e71532-e71532","source":"Crossref","is-referenced-by-count":4,"title":["AI in Clinical Decision Support Systems: Promising Applications and Strategies for Managing Data Challenges"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2472-7103","authenticated-orcid":false,"given":"Jennifer E","family":"Daly","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8857-5148","authenticated-orcid":false,"given":"Dursun","family":"Delen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7782-8162","authenticated-orcid":false,"given":"Zheng","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0071-9715","authenticated-orcid":false,"given":"River","family":"Smith","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5872-359X","authenticated-orcid":false,"given":"Jacqueline","family":"Honerlaw","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1727-7076","authenticated-orcid":false,"given":"Kelly","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0954-2096","authenticated-orcid":false,"given":"Bridget","family":"Bennett","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6549-4847","authenticated-orcid":false,"given":"Jennifer","family":"Sippel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,5,4]]},"reference":[{"issue":"21","key":"R1","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1001\/jama.2018.17163","article-title":"Clinical decision support in the era of artificial intelligence","volume":"320","author":"Shortliffe","journal-title":"JAMA"},{"issue":"7639","key":"R2","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","journal-title":"Nature"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-018-0029-1","article-title":"Scalable and accurate deep learning with electronic health records","volume":"1","author":"Rajkomar","journal-title":"NPJ Digit Med"},{"key":"R4","doi-asserted-by":"publisher","DOI":"10.2196\/55315","article-title":"Clinical decision support and natural language processing in medicine: systematic literature review","volume":"26","author":"Eguia","journal-title":"J Med Internet Res"},{"issue":"1","key":"R5","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-019-1426-2","article-title":"Key challenges for delivering clinical impact with artificial intelligence","volume":"17","author":"Kelly","journal-title":"BMC Med"},{"issue":"4","key":"R6","doi-asserted-by":"publisher","first-page":"119","DOI":"10.3390\/informatics12040119","article-title":"Explainable AI for clinical decision support systems: literature review, key gaps, and research synthesis","volume":"12","author":"Salimparsa","journal-title":"Informatics"},{"key":"R7","doi-asserted-by":"publisher","DOI":"10.2196\/69678","article-title":"Trust in artificial intelligence-based clinical decision support systems among health care workers: systematic review","volume":"27","author":"Tun","journal-title":"J Med Internet Res"},{"key":"R8","doi-asserted-by":"publisher","DOI":"10.2196\/57224","article-title":"Predictors of health care practitioners\u2019 intention to use ai-enabled clinical decision support systems: meta-analysis based on the unified theory of acceptance and use of technology","volume":"26","author":"Dingel","journal-title":"J Med Internet Res"},{"key":"R9","doi-asserted-by":"publisher","DOI":"10.2196\/69688","article-title":"Improving AI-based clinical decision support systems and their integration into care from the perspective of experts: interview study among different stakeholders","volume":"13","author":"Giebel","journal-title":"JMIR Med Inform"},{"key":"R10","unstructured":"Gambetti A Han Q Shen H Soares C . 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