OBJECTIVES: The research aims to develop strategies to overcome the limitations of manual testing through the implementation of AI technologies in genomic biomarker testing, aiming to enhance the precision and efficiency of oncology diagnostics in the field of precision oncology.

METHODS: The method involved literature search through artificial intelligence tool MaiA on PubMed database using search strategy Population, Concept, Context (PCC) criteria. Eligible study designs included clinical trials, observational studies, reviews, systematic literature reviews, and meta-analyses from January 1,2013 till present, excluding treatment and imaging methods. Data charting involved extracting key information such as cancer type, AI tools used, genomic marker type, key findings, and future directions from the selected studies.

RESULTS: Among the initial pool of 391 screened articles, 40 full-text articles were reviewed for data charting. The application of AI in analyzing genetic data has demonstrated its potential to enhance diagnostic precision. Our findings reveal the utilization of various AI algorithms to integrate diverse data sources, to generate comprehensive patient profiles and personalized treatment strategies included support vector machines (SVM) for distinguishing melanoma from soft tissue sarcoma, convolutional neural networks (CNN) and Cox regression models in breast cancer detection, and genetic algorithms in bladder, breast, ovarian, liver, colorectal, and leukemia cancers. Challenges included the need for standardized data formats, robust validation methods, and regulatory considerations. Furthermore, ethical and privacy concerns surrounding the use of AI in healthcare necessitate careful attention. AI also aids in analyzing coding variants and establishing genotype-phenotype relationships, further improving genomic testing precision.

CONCLUSIONS: The potential of AI in genomic biomarker testing for precision oncology is promising, as it can enhance precision, effectiveness, and personalized treatment options. The advancements in AI are transforming oncology, introducing novel tools for cancer detection, personalized treatment, patient care management, and various other aspects of the field.