Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment

Figure 1 Number of yearly publications. A: The number of google scholar articles featuring the keyword “Pharmacogenomics testing (MESH)”, “precision medicine”, artificial intelligence and ”Machine learning” ’ in the title or abstract (years 1980-2023); B: The number of in google scholar articles featuring the keyword “Pharmacogenomics testing (MESH)”,“precision medicine”, psychiatric treatment “, artificial intelligence and ”Machine learning” ’ in the title or abstract (years 1980-2023).

Figure 2 Schematic of artificial intelligence as a superset of machine learning, ANFIS, ANN, MLR and PL are examples of machine learning models which are acronyms for adaptive neuro-fuzzy inference system, artificial neural network, multiple linear regression, and power law, respectively.

Figure 3 Example of clinical pharmacogenetics implementation consortium point of care clinical decision support for an antidepressant with multiple gene interactions.

Figure 4 Challenges with Genomic psychiatric treatment and prospects for artificial intelligence integration. ¹See Pre-Test Alert tab for pre-test alert examples. ²Additional actions may include ordering a PGx test, preventing the clinician form ordering the medication, or allowing the clinician to cancel out the alert. ³Priority test result is defined as a genetic test that necessitates a change in drug, drug dose or drug monitoring. ⁴See Post-Test Alert tab for post-test alert examples. Blue color indicates further action may be required.