5. Descriptive phrases that suggested at least comparable (or better) diagnostic performance of an algorithm to a clinician were found in most abstracts, despite studies having overt limitations in design, reporting, transparency and risk of bias. Qualifying statements about the need for further prospective testing were rarely offered in study abstractsand werent mentioned at all in some 23 studies that claimed superior performance to a clinician, the authors report. Accepting that abstracts are usually word limited, even in the discussion sections of the main text, nearly two thirds of studies failed to make an explicit recommendation for further prospective studies or trials, the authors write. Although it is clearly beyond the power of authors to control how the media and public interpret their findings, judicious and responsible use of language in studies and press releases that factor in the strength and quality of the evidence can help.

Expounding on the latter point in their concluding section, Nagendran et al. reiterate that using overpromising language in studies involving AI-human comparisons might inadvertently mislead the media and the public, and potentially lead to the provision of inappropriate care that does not align with patients best interests.

The development of a higher quality and more transparently reported evidence base moving forward, they add, will help to avoid hype, diminish research waste and protect patients.

The study is available in full for free.

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5 findings that could spur imaging AI researchers to 'avoid hype, diminish waste and protect patients' - Health Imaging