Machine Learning, a fast-growing technology, is an application of Artificial Intelligence that has provided important contributes to drug discovery and clinical development. In the last few years, the number of clinical applications based on Machine Learning has been constantly growing and this is now affecting the National Competent Authorities during the assessment of most recently submitted Clinical Trials that are designed, managed or that are generating data deriving from the use of Machine Learning or Artificial Intelligence technologies. We review current information available on the regulatory approach to Clinical Trials and Machine Learning. We also provide inputs for further reasoning and potential indications, including six actionable proposals for regulators to proactively drive the upcoming evolution of Clinical Trials within a strong regulatory frame-work, focusing on patient's safety, health protection and fostering immediate access to effective treatments.

Clinical Trials and Machine Learning: Regulatory Approach Review / Dri, Diego Alejandro; Massella, Maurizio; Gramaglia, Donatella; Marianecci, Carlotta; Petraglia, Sandra. - In: REVIEWS ON RECENT CLINICAL TRIALS. - ISSN 1574-8871. - 16:4(2021), pp. 341-350-350. [10.2174/1574887116666210715114203]

Clinical Trials and Machine Learning: Regulatory Approach Review

Dri, Diego Alejandro
;
Marianecci, Carlotta;
2021

Abstract

Machine Learning, a fast-growing technology, is an application of Artificial Intelligence that has provided important contributes to drug discovery and clinical development. In the last few years, the number of clinical applications based on Machine Learning has been constantly growing and this is now affecting the National Competent Authorities during the assessment of most recently submitted Clinical Trials that are designed, managed or that are generating data deriving from the use of Machine Learning or Artificial Intelligence technologies. We review current information available on the regulatory approach to Clinical Trials and Machine Learning. We also provide inputs for further reasoning and potential indications, including six actionable proposals for regulators to proactively drive the upcoming evolution of Clinical Trials within a strong regulatory frame-work, focusing on patient's safety, health protection and fostering immediate access to effective treatments.
2021
Artificial intelligence; big data; clinical trials; digital medicine; drug discovery; machine learning; regulatory.
01 Pubblicazione su rivista::01g Articolo di rassegna (Review)
Clinical Trials and Machine Learning: Regulatory Approach Review / Dri, Diego Alejandro; Massella, Maurizio; Gramaglia, Donatella; Marianecci, Carlotta; Petraglia, Sandra. - In: REVIEWS ON RECENT CLINICAL TRIALS. - ISSN 1574-8871. - 16:4(2021), pp. 341-350-350. [10.2174/1574887116666210715114203]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1606405
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