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Artificial Intelligence, Medications, Pharmacogenomics, and Ethics

Overview
Specialties Genetics
Pharmacology
Date 2024 Nov 15
PMID 39545629
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Abstract

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing various scientific and clinical disciplines including pharmacogenomics (PGx) by enabling the analysis of complex datasets and the development of predictive models. The integration of AI and ML with PGx has the potential to provide more precise, data-driven insights into new drug targets, drug efficacy, drug selection, and risk of adverse events. While significant effort to develop and validate these tools remain, ongoing advancements in AI technologies, coupled with improvements in data quality and depth is anticipated to drive the transition of these tools into clinical practice and delivery of individualized treatments and improved patient outcomes. The successful development and integration of AI-assisted PGx tools will require careful consideration of ethical, legal, and social issues (ELSI) in research and clinical practice. This paper explores the intersection of PGx with AI, highlighting current research and potential clinical applications, and ELSI including privacy, oversight, patient and provider knowledge and acceptance, and the impact on patient-provider relationship and new roles.

References
1.
Tremmel R, Pirmann S, Zhou Y, Lauschke V . Translating pharmacogenomic sequencing data into drug response predictions-How to interpret variants of unknown significance. Br J Clin Pharmacol. 2023; 91(2):252-263. PMC: 11773106. DOI: 10.1111/bcp.15915. View

2.
Shevtsova D, Ahmed A, Boot I, Sanges C, Hudecek M, Jacobs J . Trust in and Acceptance of Artificial Intelligence Applications in Medicine: Mixed Methods Study. JMIR Hum Factors. 2024; 11:e47031. PMC: 10831593. DOI: 10.2196/47031. View

3.
Walters W, Barzilay R . Critical assessment of AI in drug discovery. Expert Opin Drug Discov. 2021; 16(9):937-947. DOI: 10.1080/17460441.2021.1915982. View

4.
Taliaz D, Spinrad A, Barzilay R, Barnett-Itzhaki Z, Averbuch D, Teltsh O . Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data. Transl Psychiatry. 2021; 11(1):381. PMC: 8266902. DOI: 10.1038/s41398-021-01488-3. View

5.
Zhang Z, Wei X . Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy. Semin Cancer Biol. 2023; 90:57-72. DOI: 10.1016/j.semcancer.2023.02.005. View