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Clinician-Facing Artificial Intelligence-Based Clinical Decision Support Systems Evaluated in Real-World Health Care Settings: A PubMed/MEDLINE-Based, PMC-Weighted Scoping Review and Evidence Map

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…

By the Truvace desk2 min read1 sourcePeer-reviewedTRV-2026-1613· cite & verify
Clinician-Facing Artificial Intelligence-Based Clinical Decision Support Systems Evaluated in Real-World Health Care Settings: A PubMed/MEDLINE-Based, PMC-Weighted Scoping Review and Evidence Map
HEMOS: an expert support system prototype for forecasting blood requirements for Marine Corps medical support/ by Greenauer, Michael A.. Public domain

In brief

A scoping review searched PubMed/MEDLINE on 15 June 2026 for studies from 2020 to 2026 of clinician-facing AI decision support tools assessed in real-world health care settings. It screened 14,083 unique records and included 1181 reports, then mapped their applications, evaluation approaches, integration and outcomes.

The mapping matters because clinical value depends on more than technical performance, and most of the evidence stops at validation. Uncertainty remains about patient benefit, safe deployment and equity, and the screening was largely AI-assisted without full dual-human review, so the map should be read with that in mind.

Main points

  1. Of the 1181 included reports, 741 (62.7%) were at the clinical validation stage.
  2. Prospective or user evaluation accounted for 270 reports (22.9%), and implementation or impact evaluation for 170 (14.4%).
  3. External validation signals appeared in 441 reports (37.3%) and temporal validation in only 27 (2.3%).
  4. Safety signals appeared in 98 reports (8.3%) and fairness or equity signals in 61 (5.2%).
  5. The authors call for fuller reporting on technical integration, explainability, economics and post-deployment governance.

The problem

Among 1181 included reports on clinician-facing AI decision support, most stopped at clinical validation, and few evaluated implementation, safety, fairness or economics, so patient benefit is not yet demonstrated.

The rundown

The search used the same strategy for each publication year from 2020 to 2026, changing only the date range. After duplicate removal and rule-based prescreening, 5140 reports were sought and 3767 were considered for eligibility, and 1181 were included.

A minimal duplicate-report check mapped the included reports to 1180 candidate report families. Internal validation signals were found in 707 reports (59.9%) and multicenter validation in 312 (26.4%).

Explainability or interpretability signals appeared in 155 reports (13.1%) and economic or resource evaluation in 182 (15.4%). The authors say more comprehensive evaluation is needed across settings, populations, devices and clinical workflows.

Sources

  1. Peer-reviewedThe International Journal of Frontier Sciences2026-10-15

The debate