Periodic Manual Algorithm Updates and Generalizability: A Developer’s Response. Comment on “Evaluation of Four Artificial Intelligence–Assisted Self-Diagnosis Apps on Three Diagnoses: Two-Year Follow-Up Study”

Gilbert, Stephen; Fenech, Matthew; Idris, Anisa; Türk, Ewelina

doi:10.2196/26514

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…This raises the question, “does the current legislation provide a regulatory approval framework for ML-based SaMD?” Although burdensome, the current EU approach can be (and has been) adopted for ML-based SaMD (see [ 20 ]). SaMD manufacturers can optimize their software development processes to maximize their efficiency in this linear process, particularly for documenting the effects of model change on SaMD performance between updates (these are aspects of “change control,” a fundamental principle of medical device quality management systems).…”

Section: Limitations Of Current Approachesmentioning

confidence: 99%

Algorithm Change Protocols in the Regulation of Adaptive Machine Learning–Based Medical Devices

Gilbert¹,

Fenech²,

Hirsch³

et al. 2021

J Med Internet Res

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One of the greatest strengths of artificial intelligence (AI) and machine learning (ML) approaches in health care is that their performance can be continually improved based on updates from automated learning from data. However, health care ML models are currently essentially regulated under provisions that were developed for an earlier age of slowly updated medical devices—requiring major documentation reshape and revalidation with every major update of the model generated by the ML algorithm. This creates minor problems for models that will be retrained and updated only occasionally, but major problems for models that will learn from data in real time or near real time. Regulators have announced action plans for fundamental changes in regulatory approaches. In this Viewpoint, we examine the current regulatory frameworks and developments in this domain. The status quo and recent developments are reviewed, and we argue that these innovative approaches to health care need matching innovative approaches to regulation and that these approaches will bring benefits for patients. International perspectives from the World Health Organization, and the Food and Drug Administration’s proposed approach, based around oversight of tool developers’ quality management systems and defined algorithm change protocols, offer a much-needed paradigm shift, and strive for a balanced approach to enabling rapid improvements in health care through AI innovation while simultaneously ensuring patient safety. The draft European Union (EU) regulatory framework indicates similar approaches, but no detail has yet been provided on how algorithm change protocols will be implemented in the EU. We argue that detail must be provided, and we describe how this could be done in a manner that would allow the full benefits of AI/ML-based innovation for EU patients and health care systems to be realized.

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Section: Limitations Of Current Approachesmentioning

confidence: 99%

Algorithm Change Protocols in the Regulation of Adaptive Machine Learning–Based Medical Devices

Gilbert¹,

Fenech²,

Hirsch³

et al. 2021

J Med Internet Res

Self Cite

View full text Add to dashboard Cite

show abstract

“…I would like to thank Gilbert et al [ 1 ] for their useful contributions and tips to help improve our work [ 2 ].…”

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confidence: 99%

Author's Reply to: Periodic Manual Algorithm Updates and Generalizability: A Developer’s Response. Comment on “Evaluation of Four Artificial Intelligence–Assisted Self-Diagnosis Apps on Three Diagnoses: Two-Year Follow-Up Study”

Ćirković¹

2021

J Med Internet Res

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Predicting the therapeutic efficacy of AIT for asthma using clinical characteristics, serum allergen detection metrics, and machine learning techniques

Yao,

Wang,

Zhou

et al. 2023

Computers in Biology and Medicine

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Periodic Manual Algorithm Updates and Generalizability: A Developer’s Response. Comment on “Evaluation of Four Artificial Intelligence–Assisted Self-Diagnosis Apps on Three Diagnoses: Two-Year Follow-Up Study”

Cited by 4 publications

References 4 publications

Algorithm Change Protocols in the Regulation of Adaptive Machine Learning–Based Medical Devices

Algorithm Change Protocols in the Regulation of Adaptive Machine Learning–Based Medical Devices

Author's Reply to: Periodic Manual Algorithm Updates and Generalizability: A Developer’s Response. Comment on “Evaluation of Four Artificial Intelligence–Assisted Self-Diagnosis Apps on Three Diagnoses: Two-Year Follow-Up Study”

Predicting the therapeutic efficacy of AIT for asthma using clinical characteristics, serum allergen detection metrics, and machine learning techniques

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