{
    "abstract": "Artificial Intelligence (AI) has been shown to enhance fracture-detection-accuracy, but the most effective AI-implementation in clinical practice is less well understood. In the current study, four approaches to AI-implementation are evaluated for their impact on AI-effectiveness.\nRetrospective single-center study based on all consecutive, around-the-clock radiographic examinations for suspected fractures, and accompanying clinical-practice radiologist-diagnoses, between January and March 2023. These image-sets were independently analysed by a dedicated bone-fracture-detection-AI. Findings were combined with radiologist clinical-practice diagnoses to simulate the four AI-implementation methods deemed most relevant to clinical workflows: AI-standalone (radiologist-findings not consulted); AI-problem-solving (AI-findings consulted when radiologist in doubt); AI-triage (radiologist-findings consulted when AI in doubt); and AI-safety net (AI-findings consulted when radiologist diagnosis negative). Reference-standard diagnoses were established by two senior musculoskeletal-radiologists (by consensus in cases of disagreement). Radiologist- and radiologist\u00a0+\u00a0AI diagnoses were compared for false negatives (FN), false positives (FP) and their clinical consequences. Experience-level-subgroups radiologists-in-training-, non-musculoskeletal-radiologists, and dedicated musculoskeletal-radiologists were analysed separately.\n1508 image-sets were included (1227 unique patients; 40 radiologist-readers). Radiologist results were: 2.7\u00a0% FN (40/1508), 28 with clinical consequences; 1.2\u00a0% FP (18/1508), 2 received full-fracture treatments (11.1\u00a0%). All AI-implementation methods changed overall FN and FP with statistical significance (p\u00a0<\u00a00.001): AI-standalone 1.5\u00a0% FN (23/1508; 11 consequences), 6.8\u00a0% FP (103/1508); AI-problem-solving 3.2\u00a0% FN (48/1508; 31 consequences), 0.6\u00a0% FP (9/1508); AI-triage 2.1\u00a0% FN (32/1508; 18 consequences), 1.7\u00a0% FP (26/1508); AI-safety net 0.07\u00a0% FN (1/1508; 1 consequence), 7.6\u00a0% FP (115/1508). Subgroups show similar trends, except AI-triage increased FN for all subgroups except radiologists-in-training.\nImplementation methods have a large impact on AI-effectiveness. These results suggest AI should not be considered for problem-solving or triage at this time; AI standalone performs better than either and may be a source of assistance where radiologists are unavailable. Best results were obtained implementing AI as safety net, which eliminates missed fractures with serious clinical consequences; even though false positives are increased, unnecessary treatments are limited.",
    "authors": [
        {
            "affiliation": "Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, the Netherlands; CARIM School for Cardiovascular Diseases, Maastricht University, the Netherlands.",
            "firstname": "Daan V",
            "initials": "DV",
            "lastname": "Loeffen"
        },
        {
            "affiliation": "Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, the Netherlands; CARIM School for Cardiovascular Diseases, Maastricht University, the Netherlands; CAPHRI Care and Public Health Research Institute, Maastricht University, the Netherlands.",
            "firstname": "Frank M",
            "initials": "FM",
            "lastname": "Zijta"
        },
        {
            "affiliation": "CAPHRI Care and Public Health Research Institute, Maastricht University, the Netherlands; Department of Orthopaedic Surgery, Maastricht University Medical Centre, the Netherlands.",
            "firstname": "Tim A",
            "initials": "TA",
            "lastname": "Boymans"
        },
        {
            "affiliation": "Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, the Netherlands; CARIM School for Cardiovascular Diseases, Maastricht University, the Netherlands.",
            "firstname": "Joachim E",
            "initials": "JE",
            "lastname": "Wildberger"
        },
        {
            "affiliation": "Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, the Netherlands; CARIM School for Cardiovascular Diseases, Maastricht University, the Netherlands. Electronic address: estelle.nijssen@mumc.nl.",
            "firstname": "Estelle C",
            "initials": "EC",
            "lastname": "Nijssen"
        }
    ],
    "conclusions": "Implementation methods have a large impact on AI-effectiveness. These results suggest AI should not be considered for problem-solving or triage at this time; AI standalone performs better than either and may be a source of assistance where radiologists are unavailable. Best results were obtained implementing AI as safety net, which eliminates missed fractures with serious clinical consequences; even though false positives are increased, unnecessary treatments are limited.",
    "copyrights": "Copyright \u00a9 2025 The Author(s). Published by Elsevier B.V. All rights reserved.",
    "doi": "10.1016/j.ejrad.2025.112113",
    "journal": "European journal of radiology",
    "keywords": [
        "AI",
        "Automated bone-fracture detection",
        "Diagnostic imaging",
        "Digital x-ray",
        "Emergency medicine",
        "Fractures",
        "Humans",
        "Implementation",
        "MSK",
        "Radiologists"
    ],
    "methods": null,
    "publication_date": "2025-04-20",
    "pubmed_id": "40252277",
    "results": "1508 image-sets were included (1227 unique patients; 40 radiologist-readers). Radiologist results were: 2.7\u00a0% FN (40/1508), 28 with clinical consequences; 1.2\u00a0% FP (18/1508), 2 received full-fracture treatments (11.1\u00a0%). All AI-implementation methods changed overall FN and FP with statistical significance (p\u00a0<\u00a00.001): AI-standalone 1.5\u00a0% FN (23/1508; 11 consequences), 6.8\u00a0% FP (103/1508); AI-problem-solving 3.2\u00a0% FN (48/1508; 31 consequences), 0.6\u00a0% FP (9/1508); AI-triage 2.1\u00a0% FN (32/1508; 18 consequences), 1.7\u00a0% FP (26/1508); AI-safety net 0.07\u00a0% FN (1/1508; 1 consequence), 7.6\u00a0% FP (115/1508). Subgroups show similar trends, except AI-triage increased FN for all subgroups except radiologists-in-training.",
    "title": "AI for fracture diagnosis in clinical practice: Four approaches to systematic AI-implementation and their impact on AI-effectiveness.",
    "xml": "<Element 'PubmedArticle' at 0x788504d86700>"
}