AI in radiology: is it the time for randomized controlled trials?

Gorenstein, Larisa; Soffer, Shelly; Apter, Sara; Konen, Eli; Klang, Eyal

doi:10.1007/s00330-022-09381-3

Cited by 3 publications

(1 citation statement)

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“…In recent years, the medical and scientific communities have increasingly emphasized the need for rigorous evaluation and validation of AI technologies before their integration into clinical practice. Numerous studies and commentaries have highlighted the potential risks of prematurely adopting AI-driven solutions in healthcare [9][10][11][12][13] . Concerns about reliability, safety, and ethical implications have dominated discussions, leading to calls for systematic evaluation frameworks and robust testing protocols 14 .…”

Section: Introductionmentioning

confidence: 99%

Ongoing and planned Randomized Controlled Trials of AI in medicine: An analysis of Clinicaltrials.gov registration data

Andreoletti,

Senkalfa,

Blasimme

2024

Preprint

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The integration of Artificial Intelligence (AI) technologies into clinical practice holds significant promise for revolutionizing healthcare. However, the realization of this potential requires rigorous evaluation and validation of AI applications to ensure their safety, efficacy, and clinical significance. Despite increasing awareness of the need for robust testing, the majority of AI-related Randomized Controlled Trials (RCTs) so far have exhibited notable limitations, impeding the generalizability and proper integration of their findings into clinical settings. To understand whether the field is progressing towards more robust testing, we conducted an analysis of the registration data of ongoing and planned RCTs of AI in medicine available in theClinicaltrials.govdatabase. Our analysis highlights several key trends and challenges. Effectively addressing these challenges is essential for advancing the field of medical AI and ensuring its successful integration into clinical practice.

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Section: Introductionmentioning

confidence: 99%