AI-Assisted Clinical Decision Support Versus Standard Teleconsultation in Adult Medicine: A Systematic Review of Diagnostic Accuracy, Referral and Testing Decisions, and Clinician Confidence.
- 1Medical Engagement Department, Health Horizon Medical Co., Jeddah 21323, Saudi Arabia.
Abstract
Background/Objectives: Artificial intelligence (AI) is increasingly integrated into teleconsultation, but its effects on clinician performance remain uncertain. We assessed whether clinician-facing AI improves diagnostic accuracy, reduces referrals or testing, and increases clinician confidence compared with standard teleconsultation or conventional resources. Methods: This retrospectively registered systematic review followed the PRISMA 2020 and Synthesis Without Meta-Analysis (SWiM) guidance. PubMed/MEDLINE, publisher-indexed platforms, Google Scholar, ClinicalTrials.gov, and backward and forward citation searches were performed from inception to 1 August 2026. Eligible studies evaluated clinician-facing AI in adult primary, urgent, or general medical teleconsultation or in high-fidelity virtual consultation designs and reported diagnostic, referral/testing, or confidence outcomes. One reviewer screened records, extracted data, and assessed the risk of bias using the design-appropriate QUADAS-2, RoB 2, and ROBINS-I domains, with a second pass for consistency. Owing to clinical and methodological heterogeneity, the results were synthesized narratively. Results: Of 31 records identified, 25 remained after duplicate removal, 18 full texts were assessed, and six studies were included: two randomized virtual experiments, two retrospectives virtual care cohorts, one randomized crossover conversational AI study, and one provider-to-provider telemedicine case series. In a telehealth screening experiment, AI improved diagnostic prediction, but physicians given a large language model showed no significant benefit (adjusted difference, two percentage points; 95% CI, -4 to 8). Across 102,059 virtual encounters, an AI-generated differential included the clinician's final diagnosis in 84.2%, although the reference was not independent. AI-assisted urgent care recommendations were rated optimal more often than final physician recommendations (77.1% vs. 67.1%). AI did not reduce unnecessary referrals; one experiment found more confirmatory testing requests and lower trust than with a familiar clinical score. Certainty was low or very low for the principal clinical outcomes. Conclusions: AI may support teleconsultation decisions, but evidence that it reduces unnecessary referrals or meaningfully increases clinician confidence is insufficient. Pragmatic trials using independent patient-level reference standards are needed. Registration: This completed review was retrospectively registered on the Open Science Framework (OSF); no protocol was published before study selection.
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BibTeX
@article{srada2026aiassisted,
title = {{AI-Assisted Clinical Decision Support Versus Standard Teleconsultation in Adult Medicine: A Systematic Review of Diagnostic Accuracy, Referral and Testing Decisions, and Clinician Confidence.}},
author = {Yaslam Ba Srada and Bobakr Bawazir and Saad Mohammad Aghi and Mohamed Elsayed and Hisham Yousif and Mohamed Ibrahim and Kareem Adel Eltohamy and Mark Sim},
journal = {Clinics and practice},
year = {2026},
month = sep,
doi = {10.3390/clinpract16100181},
url = {https://europepmc.org/article/MED/42930367},
note = {CC BY},
}
RIS
TY - JOUR
TI - AI-Assisted Clinical Decision Support Versus Standard Teleconsultation in Adult Medicine: A Systematic Review of Diagnostic Accuracy, Referral and Testing Decisions, and Clinician Confidence.
AU - Yaslam Ba Srada
AU - Bobakr Bawazir
AU - Saad Mohammad Aghi
AU - Mohamed Elsayed
AU - Hisham Yousif
AU - Mohamed Ibrahim
AU - Kareem Adel Eltohamy
AU - Mark Sim
PY - 2026
DA - 2026/09/30
JO - Clinics and practice
DO - 10.3390/clinpract16100181
UR - https://europepmc.org/article/MED/42930367
AB - Background/Objectives: Artificial intelligence (AI) is increasingly integrated into teleconsultation, but its effects on clinician performance remain uncertain. We assessed whether clinician-facing AI improves diagnostic accuracy, reduces referrals or testing, and increases clinician confidence compared with standard teleconsultation or conventional resources. Methods: This retrospectively registered systematic review followed the PRISMA 2020 and Synthesis Without Meta-Analysis (SWiM) guidance. PubMed/MEDLINE, publisher-indexed platforms, Google Scholar, ClinicalTrials.gov, and backward and forward citation searches were performed from inception to 1 August 2026. Eligible studies evaluated clinician-facing AI in adult primary, urgent, or general medical teleconsultation or in high-fidelity virtual consultation designs and reported diagnostic, referral/testing, or confidence outcomes. One reviewer screened records, extracted data, and assessed the risk of bias using the design-appropriate QUADAS-2, RoB 2, and ROBINS-I domains, with a second pass for consistency. Owing to clinical and methodological heterogeneity, the results were synthesized narratively. Results: Of 31 records identified, 25 remained after duplicate removal, 18 full texts were assessed, and six studies were included: two randomized virtual experiments, two retrospectives virtual care cohorts, one randomized crossover conversational AI study, and one provider-to-provider telemedicine case series. In a telehealth screening experiment, AI improved diagnostic prediction, but physicians given a large language model showed no significant benefit (adjusted difference, two percentage points; 95% CI, -4 to 8). Across 102,059 virtual encounters, an AI-generated differential included the clinician's final diagnosis in 84.2%, although the reference was not independent. AI-assisted urgent care recommendations were rated optimal more often than final physician recommendations (77.1% vs. 67.1%). AI did not reduce unnecessary referrals; one experiment found more confirmatory testing requests and lower trust than with a familiar clinical score. Certainty was low or very low for the principal clinical outcomes. Conclusions: AI may support teleconsultation decisions, but evidence that it reduces unnecessary referrals or meaningfully increases clinician confidence is insufficient. Pragmatic trials using independent patient-level reference standards are needed. Registration: This completed review was retrospectively registered on the Open Science Framework (OSF); no protocol was published before study selection.
SN - 2039-7275
SN - 2039-7283
ER -