Background The advent of artificial intelligence (AI) tools in oncology to support clinical decision-making, reduce physician workload and automate workflow inefficiencies yields both great promise and caution. To generate high-quality evidence on the safety and efficacy of AI interventions, randomised controlled trials (RCTs) remain the gold standard. However, the completeness and quality of reporting among AI trials in oncology remains unknown. Objective This systematic review investigates the reporting concordance of RCTs for AI interventions in oncology using the CONSORT (Consolidated Standards of Reporting Trials) 2010 and CONSORT-AI 2020 extension guideline and comprehensively summarises the state of AI RCTs in oncology. Methods and analysis We queried OVID MEDLINE and Embase on 22 October 2024 using AI, cancer and RCT search terms. Studies were included if they reported on an AI intervention in an RCT including participants with cancer. Results This study included 57 RCTs of AI interventions in oncology that were primarily focused on screening (54%) or diagnosis (19%) and intended for clinician use (88%). Among all 57 RCTs, median concordance with CONSORT 2010 and CONSORT-AI 2020 was 82%. Compared with trials published before the release of CONSORT-AI (n=8), trials published after the release of CONSORT-AI (n=49) had lower median overall CONSORT (82% vs 92%) and CONSORT 2010 (81% vs 92%) concordance but similar CONSORT-AI median concordance (93% vs 93%). Guideline items related to study methodology necessary for reproducibility using the AI intervention, such as input data inclusion and exclusion, algorithm version, low quality data handling, assessment of performance error and data accessibility, were consistently under-reported. When stratifying included trials by their overall risk of bias, trials at serious risk of bias (57%) were less concordant to CONSORT guidelines compared with trials at moderate (71%) or low (84%) risk of bias. Conclusion Although the majority of CONSORT and CONSORT-AI items were well-reported, critical gaps related to reporting of methodology, reproducibility and harms persist. Addressing these gaps through consideration of trial design to mitigate risks of bias coupled with standardised reporting is one step towards responsible adoption of AI to improve patient outcomes in oncology.
Chen et al. (Fri,) studied this question.