Promise and Pitfalls of AI in Healthcare: A Critical Review - Khan, Murad; Sherani, Abdul Mannan Khan (International Journal of Multidisciplinary Sciences and Arts, 2024)
AI is already becoming a disruptive technology in healthcare in so far as this can be explored as a means for improving diagnosis, individualizing treatment, improving operational output and quickly determining new treatments. Some of the concentrations as part of AI that is today creating impressive effects across diverse healthcare sectors include; machine learning and natural language processing as well as computer vision in medical imaging, clinical support, and operation. The above innovations have viable capacity to offer enhanced value delivery, cost efficiencies, and better organizational performance for the patients. However, there is a considerable cost for adopting AI in the health care system which undoubtedly has the following disadvantages: data protection, algorithms, openness, responsibility. If bioethics AI practitioners do not take time and think through the many consequential ethical concerns related to anatomical artificial intelligence, the viewer’s cannot get an experience that is similar to that of the virtual superintendents. Thus the nature of the interaction will become more important into the future while the interaction will be based on the further evolution of algorithms then introduction of AI in precise medicine, and better receptors for safety and for fairness of the algorithm. Besides applying the explained AI methods, a significant level of IT training of healthcare professionals is also required; also, the patients themselves should be told about the advantages and disadvantages of the employed AI techniques. This review discusses strengths and weaknesses of applying AI processes in health care, problems in this sphere and tendencies of AI’s further development about seeing the integration in health-care systems. The future of applying the AI in international healthcare will therefore call for increased collaboration among researcher, policy makers and clinicians for the implementation to commence.