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Background: Clinician-facing artificial intelligence-based clinical decision support systems (AI-CDSSs) are increasingly evaluated with real-world data, but evidence on implementation, human factors, responsible AI, and patient benefit remains unclear. The objective of this research is to map applications, technical characteristics, evaluation approaches, integration, outcomes, and implementation gaps of clinician-facing AI-CDSSs in real-world health care settings. Methods: PubMed/MEDLINE was searched on 15 June 2026 using the same strategy for each publication year from 2020 to 2026, changing only the date range. After duplicate removal and rule-based prescreening, available PMC XML and outside-PMC reports were evaluated against clinical, patient-oriented, practical, and translational criteria. AI-assisted screening and charting received targeted human review and TA200/FT150/DX120 quality control; full-corpus dual-human screening was not performed. Results: Of 14,097 canonical annual records, 14,083 unique records were screened; 5140 reports were sought and 3767 were considered for eligibility. The review included 1181 reports, mapping to 1180 candidate report families in the minimal duplicate-report check. Final stages were clinical validation, 741 (62.7%); prospective/user evaluation, 270 (22.9%); and implementation/impact evaluation, 170 (14.4%). Separate internal, external, multicenter and temporal validation extraction signals were detected in 707 (59.9%), 441 (37.3%), 312 (26.4%) and 27 (2.3%) reports, respectively. Safety, fairness/equity, explainability/interpretability and economic/resource evaluation signals were detected in 98 (8.3%), 61 (5.2%), 155 (13.1%) and 182 (15.4%) reports, respectively. Conclusions: Evidence remains dominated by validation rather than prospective, implementation, and impact studies. More comprehensive evaluation and reporting of temporal validation, technical integration, safety, fairness, explainability, economics, and post-deployment governance across settings, populations, devices, and clinical workflows are needed to demonstrate accountable clinical value beyond technical functionality.

Keywords: artificial intelligence; clinician-AI interaction; clinical decision support; implementation science; machine learning; real-world evidence; responsible AI