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Regulatory Frameworks for Development and Evaluation of Artificial Intelligence–Based Diagnostic Imaging Algorithms: Summary and Recommendations
Open accessAbstract
Although artificial intelligence (AI)-based algorithms for diagnosis hold promise for improving care, their safety and effectiveness must be ensured to facilitate wide adoption. Several recently proposed regulatory frameworks provide a solid foundation but do not address a number of issues that may prevent algorithms from being fully trusted. In this article, we review the major regulatory frameworks for software as a medical device applications, identify major gaps, and propose additional strategies to improve the development and evaluation of diagnostic AI algorithms. We identify the following major shortcomings of the current regulatory frameworks: (1) conflation of the diagnostic task with the diagnostic algorithm, (2) superficial treatment of the diagnostic task definition, (3) no mechanism to directly compare similar algorithms, (4) insufficient characterization of safety and performance elements, (5) lack of resources to assess performance at each installed site, and (6) inherent conflicts of interest. We recommend the following additional measures: (1) separate the diagnostic task from the algorithm, (2) define performance elements beyond accuracy, (3) divide the evaluation process into discrete steps, (4) encourage assessment by a third-party evaluator, (5) incorporate these elements into the manufacturers' development process. Specifically, we recommend four phases of development and evaluation, analogous to those that have been applied to pharmaceuticals and proposed for software applications, to help ensure world-class performance of all algorithms at all installed sites. In the coming years, we anticipate the emergence of a substantial body of research dedicated to ensuring the accuracy, reliability, and safety of the algorithms.
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In the citing paper
“Given the high stakes nature of medical AI, human oversight is essential and increasingly required by policy makers and regulators.17 26”
From FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare (2025) · cites this paper as [17]
Experimental — accuracy not yet measuredIn the citing paper
“External validation is ideally performed by independent researchers from other institutions or settings3,8,10,16,27,68,72.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [10]
In the citing paper
“To ensure the generalizability of the AIPM to the intended healthcare setting, developers are advised to extensively validate the model for representative data from that setting6–8,10,13–15,24,27,56,64,66,68,72,77,79,87,88 (see phase 1, Representativeness).”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [10]
In the citing paper
“The literature recommends to assess generalizability on external data from a different time period, place, or healthcare setting3,7,8,10,16,17,27,56,68,72,79.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [10]
In the citing paper
“The AIPM should be evaluated according to how it interfaces with the end user, and how well the AIPM and the user perform together in a typical environment10,100,102,103.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [10]
In the citing paper
“Designing a good user interface and interaction requires careful consideration of the cognitive load of the end users10,18,68,99,100, by showing only relevant information in the right context, and by allowing adjustment of its behavior by end users99.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [10]
In the citing paper
“It was proposed that such evaluations can, for example, be done via reader and user studies10,102,103.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [10]
In the citing paper
“From a user interaction design perspective, it has been recommended that decisions are deterministic (consistently giving the same output for a certain input)10, and that updates of or adaptations to the AIPM should happen cautiously99.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [10]
In the citing paper
“These mechanisms should detect situations beyond the capabilities of the AIPM10,99, and share the confidence in the predictions with the user10,22,26,99.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [10]
In the citing paper
“Finally, as monitoring and auditing of AIPMs in practice are widely recommended (covered in more detail in phase 6), the developed software should facilitate this10,22,26,32,58,62,104.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [10]
In the citing paper
“When new software vulnerabilities come to light, they should be documented and reported32,61, and so should any changes made to the AIPM in response to an attack after thorough testing10,23,35,61.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [10]
In the citing paper
“Deliberate stress tests like load testing, penetration testing, integration testing and unit testing are important for the verification of the AIPM from a software perspective10,26,35,46,66,79.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [10]
In the citing paper
“This means adequate logging and traceability of predictions and decisions is required and the AIPM interface should provide sharing of performance data with end users to enable ongoing monitoring of both individual and aggregated cases, quickly highlighting any significant deviations in performance10,26,61,66.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [10]
In the citing paper
“A feasibility study or implementation pilot is recommended preceding an impact study to ensure correct and safe use in healthcare practice10,18,72.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [10]
In the citing paper
“Comparable to the feasibility study of phase 5, pilot studies are recommended to examine the potential pitfalls during implementation, considering both software and hardware issues10,18,72.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [10]
In the citing paper
“Second, the monitoring of dataset shift is also repeatedly mentioned in the literature5,10,22,72,79,91.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [10]
In the citing paper
“Developers are recommended to regularly update their AIPMs over time to improve the AIPM’s predictive performance as new improvements become available and to mitigate dataset shift10,19,23.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [10]
In the citing paper
“Additionally, monitoring the algorithm’s performance helps identify any potential biases or unintended consequences that may arise during its use [43].”
From Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education (2024) · cites this paper as [43]
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Metadata
- Authors
- Larson, D. B., Harvey, H., Rubin, D. L., Irani, N., Tse, J. R., & Langlotz, C. P.
- Affiliations
- Stanford Medicine (US)
- University College London (GB)
- University of Kansas Medical Center (US)
- University of California, Los Angeles (US)
- Corresponding author
- Larson, David B.
- Venue
- Journal of the American College of Radiology
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 18
- Issue
- 3
- Pages
- 413-424
- ISSN
- 1546-1440 (ISSN-L)
- PMC
- PMC7574690
- OpenAlex
- W3093602247
- Cited by
- 198
- References
- 28
- Citations by year
- 2026: 38 · 2025: 51 · 2024: 36 · 2023: 24 · 2022: 29 · 2021: 20
- FWCI
- 1.39
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the American College of Radiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- International Business Machines Corporation
- GE Healthcare
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Healthcare Regulation
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Keywords
- artificial intelligence, diagnostic imaging algorithm, regulatory framework, software as a medical device, algorithm evaluation, performance characterization, third-party evaluation, conflicts of interest, medical device regulation
- MeSH terms
- Algorithms, Artificial Intelligence, Diagnostic Imaging, Software, Reproducibility of Results
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