Artificial intelligence, medications, pharmacogenomics, and ethics.

Area of Science:

• Pharmacogenomics
• Artificial Intelligence
• Machine Learning

Background:

• Pharmacogenomics (PGx) integrates genetic information with drug response.
• AI and ML offer advanced analytical capabilities for complex biological data.
• Current PGx applications are expanding with computational advancements.

Purpose of the Study:

• To explore the intersection of AI/ML and PGx.
• To highlight current research and potential clinical applications of AI in PGx.
• To discuss the ethical, legal, and social implications (ELSI) of AI-assisted PGx.

Main Methods:

• Review of current literature on AI/ML applications in pharmacogenomics.
• Analysis of predictive modeling in drug target identification and efficacy.
• Examination of ethical, legal, and social issues related to AI in PGx.

Main Results:

• AI/ML enables sophisticated analysis of PGx datasets for predictive insights.
• Potential for AI-driven precision in drug selection, efficacy prediction, and adverse event risk assessment.
• Significant advancements are anticipated to drive clinical integration and personalized treatments.

Conclusions:

• AI and ML integration in PGx holds substantial promise for advancing personalized medicine.
• Successful clinical translation requires robust validation and careful consideration of ELSI.
• Future directions involve refining AI tools and addressing patient/provider acceptance and data privacy.