Integrating AI and Big Data in Healthcare: A Scalable Approach to Personalized Medicine

Abstract

Currently, healthcare is lacking in access to care, differing quality, and doctor shortages, which are increasing on a global scale. Some presented artificial intelligence (AI) as the solution to problems in healthcare. The state of AI in predictive and proactive interventions in healthcare and clinical decision support systems (CDSS) has been discussed. The majority of this work focuses on the progress of AI in healthcare systems ranging from radiology to surgery. Some debated how AI could be used to fill the human resource gap in medicine by integrating AI with medical professionals, enhancing diagnostics, and helping with better decision-making. Ethical implications (EI) of utilizing AI technology as an integrating part of the healthcare system have also been highlighted as it is crucial to address these issues ahead of time. Moreover, there are many challenges worldwide of implementing AI in medicine. The obstacles and caveats of AI in medicine in general, and some specific areas like radiology, pathology, dermatology, and others were talked about. Furthermore, current challenges of the field of life sciences that could be solved using algorithms, such as detection and isolation of rare cells, and analysis of multi-omics data were discussed. Analyzing the biomedical literature written in natural language for drug discovery, predicting off-target effects of specific compounds, etc., using were included as some successful implementations and techniques to facilitate AI and in biosciences. There are also some limitations and formidable obstacles of AI, such as data privacy and security issues, uncertainty in the usage of black boxes of algorithms, and the state of AI hype in validation. AI or is recognized as a branch of AI that contains algorithmic methods to think of solutions, retrieve results, and resolve scientific problems whether in healthcare or others.