Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866916038368559104 |
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| author | Smith, Aaron M. Fakhouri, Tala Zhuang, Run Walsh, Jonathan R. |
| author_facet | Smith, Aaron M. Fakhouri, Tala Zhuang, Run Walsh, Jonathan R. |
| contents | Applications of artificial intelligence (AI) in drug development continue to increase at a rapid pace. Regulatory authorities have provided increasingly clear perspectives on the use of AI in regulated applications, including recent draft guidance from FDA that provides a 7-step risk-based framework to assess AI model credibility for these cases. We present an application of AI models to prospectively reduce the planned sample size in a randomized controlled trial, using model-derived prognostic covariates. This can shorten trial timelines, enable faster decision making, and lower costs. When treatments are effective and tolerable they can be accessible to patients sooner, which is a compelling use case for the FDA guidance. We walk through each of the steps in the guidance, providing general recommendations for model development, evaluation, and approaches for sample size determination, with the intent of providing a clear set of guidelines on how to engage with the FDA guidance and advance responsible use of AI in drug development. We demonstrate the application with an example in Alzheimerś Disease. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_23246 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Smith, Aaron M. Fakhouri, Tala Zhuang, Run Walsh, Jonathan R. Applications Applications of artificial intelligence (AI) in drug development continue to increase at a rapid pace. Regulatory authorities have provided increasingly clear perspectives on the use of AI in regulated applications, including recent draft guidance from FDA that provides a 7-step risk-based framework to assess AI model credibility for these cases. We present an application of AI models to prospectively reduce the planned sample size in a randomized controlled trial, using model-derived prognostic covariates. This can shorten trial timelines, enable faster decision making, and lower costs. When treatments are effective and tolerable they can be accessible to patients sooner, which is a compelling use case for the FDA guidance. We walk through each of the steps in the guidance, providing general recommendations for model development, evaluation, and approaches for sample size determination, with the intent of providing a clear set of guidelines on how to engage with the FDA guidance and advance responsible use of AI in drug development. We demonstrate the application with an example in Alzheimerś Disease. |
| title | Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies |
| topic | Applications |
| url | https://arxiv.org/abs/2605.23246 |