Randomized Clinical Trials of Machine Learning Interventions in Health Care : A Systematic Review

Key Points

Question

How are machine learning interventions being incorporated into randomized clinical trials (RCTs) in health care?

Findings

In this systematic review of 41 RCTs of machine learning interventions, despite the large number of medical machine learning–based algorithms in development, few RCTs for these technologies have been conducted. Of published RCTs, most did not fully adhere to accepted reporting guidelines and had limited inclusion of participants from underrepresented minority groups.

Meaning

These findings highlight areas of concern regarding the quality of medical machine learning RCTs and suggest opportunities to improve reporting transparency and inclusivity, which should be considered in the design and publication of future trials.

Abstract

Importance

Despite the potential of machine learning to improve multiple aspects of patient care, barriers to clinical adoption remain. Randomized clinical trials (RCTs) are often a prerequisite to large-scale clinical adoption of an intervention, and important questions remain regarding how machine learning interventions are being incorporated into clinical trials in health care.

Objective

To systematically examine the design, reporting standards, risk of bias, and inclusivity of RCTs for medical machine learning interventions.

Evidence Review

In this systematic review, the Cochrane Library, Google Scholar, Ovid Embase, Ovid MEDLINE, PubMed, Scopus, and Web of Science Core Collection online databases were searched and citation chasing was done to find relevant articles published from the inception of each database to October 15, 2021. Search terms for machine learning, clinical decision-making, and RCTs were used. Exclusion criteria included implementation of a non-RCT design, absence of original data, and evaluation of nonclinical interventions. Data were extracted from published articles. Trial characteristics, including primary intervention, demographics, adherence to the CONSORT-AI reporting guideline, and Cochrane risk of bias were analyzed.

Findings

Literature search yielded 19 737 articles, of which 41 RCTs involved a median of 294 participants (range, 17-2488 participants). A total of 16 RCTS (39%) were published in 2021, 21 (51%) were conducted at single sites, and 15 (37%) involved endoscopy. No trials adhered to all CONSORT-AI standards. Common reasons for nonadherence were not assessing poor-quality or unavailable input data (38 trials [93%]), not analyzing performance errors (38 [93%]), and not including a statement regarding code or algorithm availability (37 [90%]). Overall risk of bias was high in 7 trials (17%). Of 11 trials (27%) that reported race and ethnicity data, the median proportion of participants from underrepresented minority groups was 21% (range, 0%-51%).

Conclusions and Relevance

This systematic review found that despite the large number of medical machine learning–based algorithms in development, few RCTs for these technologies have been conducted. Among published RCTs, there was high variability in adherence to reporting standards and risk of bias and a lack of participants from underrepresented minority groups. These findings merit attention and should be considered in future RCT design and reporting.

Abstract

This systematic review examines the design, reporting standards, risk of bias, and inclusivity of randomized clinical trials of machine learning interventions in health care.