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Advancing cell and gene therapy: Application of AI/ML in clinical development and patient management

Kelly Speth et al.

Journal of Biopharmaceutical Statistics2026https://doi.org/10.1080/10543406.2026.2629434article
ABDC C
Weight
0.50

What the paper says

This article explores the transformative potential of artificial intelligence (AI) and machine learning (ML) in advancing cell and gene therapy (CGT) development, with an emphasis on chimeric antigen receptor (CAR) T-cell therapy. We examine how AI/ML advanced analytics can enhance CGT clinical development by optimizing patient selection with robust eligibility criteria, facilitating clinical trial site and patient recruitment, and informing study design through risk profiling and biomarker development. Furthermore, we discuss the application of AI/ML for improving patient management, focusing on personalized dosing strategies, as well as post-treatment monitoring using wearable technology and mobile health. Opportunities and challenges presented by AI/ML in addressing the complexities of CGT, and a brief review of legislation and regulatory agency policy as it relates to AI/ML, are discussed. Overall, the integration of AI/ML offers promising avenues to enhance the efficiency, quality, and equity of CGT development and patient care.

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Cite this paper

https://doi.org/https://doi.org/10.1080/10543406.2026.2629434

Or copy a formatted citation

@article{kelly2026,
  title        = {{Advancing cell and gene therapy: Application of AI/ML in clinical development and patient management}},
  author       = {Kelly Speth et al.},
  journal      = {Journal of Biopharmaceutical Statistics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/10543406.2026.2629434},
}

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Advancing cell and gene therapy: Application of AI/ML in clinical development and patient management

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.