How is Artificial Intelligence (AI) and Machine Learning (ML) expected to improve drug response predictions beyond current rule-based systems?

Answer

By analyzing complex genetic patterns alongside traditional clinical variables like age and comorbidities.

Current PGx analysis often operates on a deterministic, rule-based system (if Gene X variant is present, then response Y occurs). However, many real-world drug responses are probabilistic and influenced by numerous factors. Artificial Intelligence and Machine Learning are essential for handling this complexity. These computational approaches allow algorithms to simultaneously process vast amounts of genomic data alongside non-genetic clinical variables, such as the patient's age, concurrent diseases, and lifestyle factors. This integration allows AI/ML models to create significantly finer-grained, nuanced predictions regarding a patient's likelihood of efficacy or toxicity for drugs influenced by multiple contributing factors.

How is Artificial Intelligence (AI) and Machine Learning (ML) expected to improve drug response predictions beyond current rule-based systems?

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