{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Medical Engagement Department, Health Horizon Medical Co., Jeddah 21323, Saudi Arabia."
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yaslam Ba Srada",
      "orcid": "0009-0007-3756-4933"
    },
    {
      "affs": [
        0
      ],
      "name": "Bobakr Bawazir",
      "orcid": "0009-0004-3615-1668"
    },
    {
      "affs": [
        0
      ],
      "name": "Saad Mohammad Aghi",
      "orcid": "0009-0003-5464-2201"
    },
    {
      "affs": [
        0
      ],
      "name": "Mohamed Elsayed",
      "orcid": "0009-0002-0823-3282"
    },
    {
      "affs": [
        0
      ],
      "name": "Hisham Yousif",
      "orcid": "0009-0007-6622-1825"
    },
    {
      "affs": [
        0
      ],
      "name": "Mohamed Ibrahim",
      "orcid": "0009-0001-4068-1504"
    },
    {
      "affs": [
        0
      ],
      "name": "Kareem Adel Eltohamy",
      "orcid": "0009-0001-9487-1338"
    },
    {
      "affs": [
        0
      ],
      "name": "Mark Sim",
      "orcid": "0009-0004-7427-2401"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/663f84hp147jv.bib",
  "cited_by": 0,
  "doi": "10.3390/clinpract16100181",
  "fulltext_url": "https://europepmc.org/articles/PMC13703382",
  "id": "663f84hp147jv",
  "issns": [
    "2039-7275",
    "2039-7283"
  ],
  "landing_url": "https://europepmc.org/article/MED/42930367",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7214072373",
  "pdf_url": "https://europepmc.org/articles/PMC13703382?pdf=render",
  "pmcid": "PMC13703382",
  "pmid": "42930367",
  "published": "2026-09-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/663f84hp147jv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:01Z",
      "native_id": "MED:42930367",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42930367"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "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.",
  "title_text": "AI-Assisted Clinical Decision Support Versus Standard Teleconsultation in Adult Medicine: A Systematic Review of Diagnostic Accuracy, Referral and Testing Decisions, and Clinician Confidence.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:22:01Z",
  "url": "https://research.draconitic.com/paper/663f84hp147jv",
  "venue": "Clinics and practice",
  "versions": []
}