Harnessing Artificial Intelligence in Healthcare Analytics: From Diagnosis to Treatment Optimization

Tushar Khinvasara *

Medical Device and Pharmaceutical Manufacturing, USA.

Kimberly Morton Cuthrell

Saint James School of Medicine, United States.

Nikolaos Tzenios

Public Health and Medical Research, Charisma University, Grace Bay, Turks and Caicos Islands.

*Author to whom correspondence should be addressed.


The use of artificial intelligence (AI) in healthcare analytics has brought about a transformation in the medical field of diagnosis and treatment optimization. AI technologies have unmatched abilities in processing substantial amounts of medical data and extracting valuable insights by combining big data analytics and advanced machine learning algorithms. AI algorithms provide healthcare professionals with enhanced accuracy and speed in diagnosing illnesses, predicting patient results, and tailoring treatment plans across the entire healthcare journey. This abstract explores how artificial intelligence (AI) can revolutionize healthcare analytics in various areas such as genomics, electronic health records (EHRs), medical imaging, and clinical decision support systems. Healthcare providers can improve healthcare services by optimizing workflows, enhancing patient outcomes, and using AI-driven initiatives to make services more accessible and high in quality. In order to ensure ethical and accountable use of AI in healthcare, it is necessary to address problems such as algorithm bias, data privacy worries, and regulatory obstacles. Despite these challenges, the ongoing advancement of AI technologies has vast potential to transform patient care models and healthcare delivery methods.

Keywords: Artificial intelligence, diagnosis, treatment, healthcare, medicine, healthcare analytics

How to Cite

Khinvasara, Tushar, Kimberly Morton Cuthrell, and Nikolaos Tzenios. 2024. “Harnessing Artificial Intelligence in Healthcare Analytics: From Diagnosis to Treatment Optimization”. Asian Journal of Medicine and Health 22 (8):15-31. https://doi.org/10.9734/ajmah/2024/v22i81066.


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