Comparative Analysis and Future Research Directions in AI in Healthcare: Medical Imaging and Diagnostics - Shekh Eklakh; Sukhpreet Singh; Vijay Laxmi (International Journal of Biomedical Innovations and Engineering, 2026)
Artificial intelligence (AI) is reshaping healthcare, particularly in the areas of medical imaging and diagnostic practice. By using advanced techniques like machine learning and deep learning, AI systems help improve the accuracy, speed, and effectiveness of identifying diseases and analyzing medical images. This paper provides a comprehensive overview of the application of artificial intelligence in medical imaging and highlights its growing importance in clinical diagnostics. It discusses how AI-based systems assist healthcare professionals in analyzing medical images, detecting abnormalities at an early stage, and improving the accuracy and consistency of diagnostic decisions. Particular attention is given to the impact of AI on specialties, such as radiology and pathology, where automated image interpretation, pattern recognition, and decision-support tools are increasingly incorporated into routine practice. In addition, challenges of integrating AI into future and current healthcare delivery systems will be surveyed, including but not limited to issues associated with protecting patient data privacy and appropriate management of the large amount of private medical data that AI systems will use. Ethical issues related to the degree of transparency in decision-making, accountability, and bias in the algorithms used by AI will also be examined in this research as well as implications regarding how patients can trust the AI clinical recommendations made to them. Another significant barrier that must be addressed for responsible AI integration into healthcare involves fulfilling legal regulations and compliance requirements in various nations on a global scale. Finally, the study will also identify possibilities for future applications of AI technology in healthcare through exploratory analysis of advanced and emerging technologies, adaptive and continuous learning, and novel patient-centered clinical applications that will provide benefits such as greater personalization of medicine, improved diagnostic accuracy by virtue of the implementation of advanced technology, improved efficiency of the healthcare delivery enterprise with respect to workflow, support for clinical decisions made by healthcare providers, and overall enhancement in quality of patient care and outcome enhancement through better health.
Keywords: Computed Tomography; Mammography; Radiography; Ultrasound; X-Ray; fluoroscopy; magnetic resonance imaging; positron emission tomography; single photon emission computed tomography