Inteligencia Artificial en el desarrollo de fármacos
retos y oportunidades para su uso responsable
Abstract
Drug development is a lengthy process involving high costs and a high failure rate. Currently, the most widely used method is one based on experience and scientific literature. However, there is a large amount of healthcare data, but most of it is fragmented or isolated, and incorporating it could increase the success rates of clinical trials. Artificial Intelligence (AI) can accelerate this process with its ability to quickly analyze and integrate large amounts of data, delivering more accurate results. Furthermore, it is essential to address ethical and legal considerations when incorporating AI technologies into clinical trials. To this end, policies and safety measures are emerging for their responsible use in medical attention and to ensure safe and effective implementation.
Downloads
References
Badal, K., Lee, C. M., & Esserman, L. J. (2023). Guiding principles for the responsible development of artificial intelligence tools for healthcare. Commun Med (Lond), 3(1), 47. https://doi.org/10.1038/s43856-023-00279-9
Blanco-Gonzalez, A., Cabezon, A., Seco-Gonzalez, A., Conde-Torres, D., Antelo-Riveiro, P., Pineiro, A., & Garcia-Fandino, R. (2023). The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. Pharmaceuticals (Basel), 16(6). https://doi.org/10.3390/ph16060891
FDA (2024). “Novel Drug Approvals for 2024”. Recuperado el 15 de mayo de 2025, de https://www.fda.gov/drugs/novel-drug-approvals-fda/novel-drug-approvals-2024
Idnay, B., Butler, A., Fang, et al. (2023). Principal Investigators' Perceptions on Factors Associated with Successful Recruitment in Clinical Trials. AMIA Jt Summits Transl Sci Proc, 2023, 281-290.
Mullard, A. (2020). $1.3 billion per drug? Nat Rev Drug Discov, 19(4), 226. https://doi.org/10.1038/d41573-020-00043-x
Naik, N., Hameed, B. M. Z., et al. (2022). Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility? Front Surg, 9, 862322. https://doi.org/10.3389/fsurg.2022.862322
NCI (2024). NIH researchers develop AI tool with potential to more precisely match cancer drugs to patients. Recuperado el 20 de mayo de 2025, de https://www.cancer.gov/news-events/press-releases/2024/ai-tool-matches-cancer-drugs-to-patients
Sinha, S., Vegesna, R., et al. (2024). PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors. Nat Cancer, 5(6), 938-952. https://doi.org/10.1038/s43018-024-00756-7
Zhao, X., Iqbal, S., Valdes, I. L., Dresser, M., & Girish, S. (2022). Integrating real-world data to accelerate and guide drug development: A clinical pharmacology perspective. Clin Transl Sci, 15(10), 2293-2302. https://doi.org/10.1111/cts.13379
