Advancements and Challenges of Artificial Intelligence in Healthcare and Medicine: An Overview
Divyansh Bajpai
Department of Biotechnology, SR Institute of Management and Technology, Lucknow-226201, Uttar Pradesh, India.
Manoj Kumar Mishra
Department of Biotechnology, SR Institute of Management and Technology, Lucknow-226201, Uttar Pradesh, India.
Suyash Srivastava
Department of CSE, SR Institute of Management and Technology, Lucknow-226201, Uttar Pradesh, India.
Amit Mani Tiwari
Department of Biotechnology, Era University, Lucknow-226005, Uttar Pradesh, India.
Pankaj Gupta
Department of Biotechnology, SR Institute of Management and Technology, Lucknow-226201, Uttar Pradesh, India.
Beer Singh
Department of Electronics and Communication Engineering, SR Institute of Management and Technology, Lucknow-226201, Uttar Pradesh, India.
Sanjay Mishra
*
Department of Biotechnology, SR Institute of Management and Technology, Lucknow-226201, Uttar Pradesh, India.
*Author to whom correspondence should be addressed.
Abstract
Artificial intelligence (AI) has the potential to significantly transform medicine and enhance the experiences of patients and physicians alike. The distance between research and deployment has been shortened by promising studies and developments in medical image processing. Eventually, authors discuss significant ethical and technological difficulties ranging from racial bias to data shortage. AI may reach its full potential as these issues are resolved, making healthcare more accessible, effective, and precise for patients everywhere. However, among the excitement, there is equal scepticism, with some urging caution at inflated expectations. Conclusively, this overview provides new insights into establishing a strong platform to explore certain new facets in the area of healthcare and medicine. The rapid progression of AI technology presents an opportunity for its application in clinical practice, potentially revolutionizing healthcare services, though there are still concerns over research gaps and potential areas for further study in view of the regulation of AI in medicine and pivotal roles across the healthcare system, equally impacting patients, doctors, and researchers.
Keywords: Artificial intelligence, graph neural networks, graph transformers, healthcare knowledge graphs, medicine, multimodal learning, transfer learning