Clinical performance of automated machine learning: a systematic review

Introduction Automated machine learning (autoML) removes technical and technological barriers to building artificial intelligence models. We aimed to summarise the clinical applications of autoML, assess the capabilities of utilised platforms, evaluate the quality of the evidence trialling autoML, and gauge the performance of autoML platforms relative to conventionally developed models, as well as each other. Methods This review adhered to a PROSPERO-registered protocol (CRD42022344427). The Cochrane Library, Embase, MEDLINE, and Scopus were searched from inception to 11 July 2022. Two researchers screened abstracts and full texts, extracted data and conducted quality assessment. Disagreement was resolved through discussion and as-required arbitration by a third researcher. Results In 82 studies, 26 distinct autoML platforms featured. Brain and lung disease were the most common fields of study of 22 specialties. AutoML exhibited variable performance: AUCROC 0.35-1.00, F1-score 0.16-0.99, AUCPR 0.51-1.00. AutoML exhibited the highest AUCROC in 75.6% trials; the highest F1-score in 42.3% trials; and the highest AUCPRC in 83.3% trials. In autoML platform comparisons, AutoPrognosis and Amazon Rekognition performed strongest with unstructured and structured data respectively. Quality of reporting was poor, with a median DECIDE-AI score of 14 of 27. Conclusions A myriad of autoML platforms have been applied in a variety of clinical contexts. The performance of autoML compares well to bespoke computational and clinical benchmarks. Further work is required to improve the quality of validation studies. AutoML may facilitate a transition to data-centric development, and integration with large language models may enable AI to build itself to fulfil user-defined goals. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Protocols ### Funding Statement AJT is supported by The Royal College of Surgeons in Edinburgh (RCSED Bursary 2022), Royal College of Physicians (MSEB 2022), and Corpus Christi College, University of Cambridge (Gordon Award 1083874682). DSWT is supported by the National Medical Research Council, Singapore (NMCR/HSRG/0087/2018; MOH-000655-00; MOH-001014-00), Duke-NUS Medical School (Duke-NUS/RSF/2021/0018; 05/FY2020/EX/15-A58), and Agency for Science, Technology and Research (A20H4g2141; H20C6a0032). These funders were not involved in the conception, execution, or reporting of this study. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The raw data from this review may be provided upon request.