aceAI integration into clinical medicine across diagnostic imaging, decision support, surgery, pathology, and drug discovery
Source article: Artificial Intelligence in Clinical Medicine: Challenges Across Diagnostic Imaging, Clinical Decision Support, Surgery, Pathology, and Drug Discovery
Aims/Background: The growing integration of artificial intelligence (AI) into clinical medicine has opened new possibilities for enhancing diagnostic accuracy, therapeutic decision-making, and biomedical innovation across several domains. This review is aimed to evaluate the clinical applications of AI across five key domains of medicine: diagnostic imaging, clinical decision support systems (CDSS), surgery, pathology, and drug discovery, highlighting achievements, limitations, and future directions. Methods: A…
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A September 2025 peer-reviewed review in Clinics and Practice synthesized 150 studies of AI in clinical medicine after screening 2047 PubMed records. It found strong diagnostic imaging performance with expert-level cancer detection, promise for CDSS in predicting sepsis and atrial fibrillation, and advances in surgical guidance, pathology diagnosis, and drug discovery via protein structure prediction.
The findings matter because high diagnostic accuracy does not yet equal routine clinical benefit. The review itself notes mixed real-world outcome evidence for decision support and persistent barriers of explainability, bias, prospective validation, and regulation, leaving uncertainty about safe, equitable, patient-centered deployment without further trials and oversight.
- Review screened 2047 PubMed records and included 150 studies with patient-level outcomes across imaging, CDSS, surgery, pathology, and drug discovery.
- Diagnostic imaging showed expert-level accuracy with AUC up to 0.94 for cancer detection.
- CDSS predicted adverse events like sepsis and atrial fibrillation but real-world outcome evidence was mixed.
- Surgery, pathology, and drug discovery showed gains in intraoperative guidance, molecular inference from histology, and protein structure prediction.
Across 150 clinically validated studies, AI achieved expert-level diagnostic accuracy in imaging including cancer detection with AUC up to 0.94 and accelerated drug discovery and surgical guidance.
Integration into routine care is constrained by limited explainability, data bias, lack of prospective trials, regulatory hurdles, and mixed real-world outcome evidence for decision support tools.
The rundown
The review searched PubMed without date or language limits, yielding 2047 records, removing 243 duplicates, screening 1804 titles and abstracts, reviewing 322 full texts, and including 150 studies that met clinical validation criteria.
Data extraction focused on AI technique, dataset characteristics, comparator benchmarks, and outcomes such as diagnostic accuracy, AUC, efficiency, and clinical improvements, finding benefits in intraoperative guidance, risk stratification, and AI-assisted pathology diagnosis.
Evidence limited by lack of prospective trials, data bias, limited explainability, and regulatory hurdles, with mixed real-world outcome data for decision support.
Sources
- Peer-reviewedClinics and Practice2025-09-16
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