COMPREHENSIVE REVIEW ON THERAPEUTIC APPLICATIONS OF AI IN LEUKAEMIA: A MULTI-OMICS APPROACH

It is leukaemia, where immature white blood cells keep growing at a very fast pace and disrupt normal blood production. While there are many advances in treatment, genetics diversity, drug resistant and technological limits remain. Likewise, such AI combined with multi dimension approaches (genetics, transcriptomics, proteomics, metabolism, and epigenetics) has altered leukaemia research. Divining the diagnosis, risk assessment and precise plan for effective treatment from vast amounts of data allowed by AI is possible. In addition, it shortens the time of diagnosis, predicts responses to therapy, and explains how it fails. By integrating AI into mitigation of CML, tyrosine kinase inhibitor is first identified and targeted therapy is guided by other key biomarkers. Same is the case for AML which is mitigated by CK inhibitors and FLT3 inhibitors. Interruption and identification of various pathways by metabolomics and proteomics provide new therapeutic targets. More and more clinical studies show that AI assisted multi-omics strategies add value in making personalized care, reducing relapse, and increasing survival. Since challenges such as data integration, algorithm transparency, computational needs and ethics require collaboration among researchers, clinicians and policymakers, instant familiarity with the technology is not negligible. With the increase in AI, it will take precision medicine to the next step and change the treatment of leukaemia.