New regulatory thinking is needed for AI-based personalised drug and cell therapies in precision oncology
Open accessAbstract
Until recently the application of artificial intelligence (AI) in precision oncology was confined to activities in drug development and had limited impact on the personalisation of therapy. Now, a number of approaches have been proposed for the personalisation of drug and cell therapies with AI applied to therapy design, planning and delivery at the patient's bedside. Some drug and cell-based therapies are already tuneable to the individual to optimise efficacy, to reduce toxicity, to adapt the dosing regime, to design combination therapy approaches and, preclinically, even to personalise the receptor design of cell therapies. Developments in AI-based healthcare are accelerating through the adoption of foundation models, and generalist medical AI models have been proposed. The application of these approaches in therapy design is already being explored and realistic short-term advances include the application to the personalised design and delivery of drugs and cell therapies. With this pace of development, the limiting step to adoption will likely be the capacity and appropriateness of regulatory frameworks. This article explores emerging concepts and new ideas for the regulation of AI-enabled personalised cancer therapies in the context of existing and in development governance frameworks.
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Regulatory policies in the United States and the European Union restrict the approval of a universal multipurpose AI model.
“regulatory policies in the United States and the European Union restrict the approval of a universal multipurpose AI model, given the philosophy that medical devices should fulfill a singular purpose13.”
From Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology (2025) · cites this paper as [13]
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Inpatient and Outpatient Technologies to Assist in the Management of Insulin Dosing
Cui, L., Schroeder, P. R., & Sack, P. A. (2020) · Clinical Diabetes · doi:10.2337/cd20-0054 · Cited by 11 · see citing works
Taber, P. A., Armin, J. S., Orozco, G., Fiol, G. D., Erdrich, J., Kawamoto, K., & Israni, S. T. (2023) · Current Oncology Reports · doi:10.1007/s11912-023-01376-7 · Cited by 11 · see citing works
Quality Control of Test Systems Waived by the Clinical Laboratory Improvement Amendments of 1988
LaBeau, K. M., Simon, M. K., & Steindel, S. J. (2000) · Archives of Pathology & Laboratory Medicine · doi:10.5858/2000-124-1122-qcotsw · Cited by 10 · see citing works
Multicancer early detection tests: where are we?
Loomans-Kropp, H. A. (2022) · JNCI Cancer Spectrum · doi:10.1093/jncics/pkac084 · Cited by 8 · see citing works
Jain, M., & Spiegel, J. (2021) · eJHaem · doi:10.1002/jha2.357 · Cited by 7 · see citing works
Loeffler‐Wirth, H., Rade, M., Arakelyan, A., Kreuz, M., Loeffler, M., Koehl, U., Reiche, K., & Binder, H. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.994885 · Cited by 7 · see citing works
Self-citationJak, M., van der Velden, B. H. M., de Keizer, B., et al. (2022) · Blood · doi:10.1182/blood-2022-158543 · Cited by 3 · see citing works
CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language ModelsPreprint
Li, T., Shetty, S., Kamath, A., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2304.10946 · Cited by 2 · see citing works
Navigating Regulatory Approval for Tissue-Based Companion Diagnostics
Krueger, J. S., Lange, H., Young, G. D., et al. (2015) · Methods in pharmacology and toxicology · doi:10.1007/7653_2015_40 · Cited by 1 · see citing works
Liu, J., Whalen, C., Morrissey, S., et al. (2017) · Annals of Oncology · doi:10.1093/annonc/mdx388.020 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [71]AstraZeneca and Voluntis to test companion mobile app in ovarian cancer studies with the US National Cancer Institute. (accessed 31 July 2023); https://www.astrazeneca.com/media-centre/press-releases/2015/AstraZeneca-and-Voluntis-to-test-companion-mobile-app-in-ovarian-cancer-studies-07122015.html (2015).
- [128]Mullard A Priority review voucher pitfalls Nat. Rev. Drug Discov. 2015 14 811 811
Cited by
Sorted by citations
Ferber, D., El Nahhas, O. S. M., Wölflein, G., Wiest, I. C., Clusmann, J., Leßmann, M.-E., Foersch, S., Lammert, J., Tschochohei, M., Jäger, D., et al. (2025) · Nature Cancer · doi:10.1038/s43018-025-00991-6 · Cited by 175 · see citing works
Self-citationUse of artificial intelligence for liver diseases: A survey from the EASL congress 2024
Žigutytė, L., Sorz-Nechay, T., Clusmann, J., & Kather, J. N. (2024) · JHEP Reports · doi:10.1016/j.jhepr.2024.101209 · Cited by 33 · see citing works
Self-citation
- Authors
- Derraz, B., Breda, G., Kaempf, C., Baenke, F., Cotte, F., Reiche, K., Köhl, U., Kather, J. N., Eskenazy, D., & Gilbert, S.
- Affiliations
- Université Paris-Saclay (FR)
- Fraunhofer Institute for Cell Therapy and Immunology (DE)
- University Hospital Carl Gustav Carus (DE)
- Philipps University of Marburg (DE)
- Corresponding author
- Gilbert, Stephen
- ORCID
- Venue
- npj Precision Oncology
- Published
- 2024-01-30
- Volume
- 8
- Issue
- 1
- Pages
- 23-23
- ISSN
- 2397-768X (ISSN-L)
- PMC
- PMC10828509
- OpenAlex
- W4391360382
- Cited by
- 94
- References
- 99
- Citations by year
- 2026: 34 · 2025: 40 · 2024: 19 · 2023: 1
- FWCI
- 2.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Precision Oncology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PUBLISSO (German National Library of Medicine) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Funders
- Bundesministerium für Bildung und Forschung (16KISA100K)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Physiology › Biomedical Ethics and Regulation
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- artificial intelligence, precision oncology, personalised cancer therapy, cell therapy, regulatory framework, AI governance, foundation models, therapy design, combination therapy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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