Future direction should focus on developing robust and reliable AI models.
Artificial intelligence (AI) has gained significant attention worldwide due to its potential to revolutionize healthcare. During the past decade, AI has already been applied rapidly to numerous areas in healthcare, including medical imaging analysis, health information management, real-time decision support monitoring and disease diagnosis and treatment.
For example, AI has been integrated in cataract management to serve as a telemedicine platform for screening, diagnosing and classifying patients with cataracts, and to augment cataract surgical skill training as well as predict the duration of surgical procedures and disease progression.
AI has also shown promise in:
These and many other advancements in AI applications are very exciting. Scientific studies have demonstrated the efficient use of AI algorithms in analyzing electrocardiograms for the detection of cardiovascular diseases, and these algorithms have shown improved performance in identifying patterns and predicting events, leading to more accurate diagnoses and treatment decisions.
Additionally, AI has been utilized in precision oncology to analyze genomic sequencing data and medical imaging, and its algorithms can identify biomarkers, predict disease risk and aid in the development of targeted therapies.
It can also be used to address issues facing medical education, such as judicious information and knowledge management, and improve customer relationship management capability in healthcare by enhancing service innovation and providing access to data and health information.
AI can also design clinical trials for medical device and drug development and beyond.
These many applications of AI in improving efficiency and accuracy in diagnosis and management of diseases are fascinating and are poised to have a transformative impact on healthcare. They underscore the potential for AI to improve healthcare and services; enhance treatment outcomes and quality of life; and provide better experiences for medical professionals and patients.
Nonetheless, there are implementation barriers. Concerns over security and privacy, regulatory approval, poor generalizability of AI models, trust and explainability issues, unfavorable end-user perceptions and uncertain economic value contribute to the challenges in translating AI algorithms into clinical settings.
Additional challenges, such as the need for standardized protocols and regulations; adequate financial resources and investment; and efficient algorithms and models should be addressed for a successful transition to the real-world implementation of AI.
Furthermore, even though many medical workers had a relatively high acceptance level towards implementation of AI in healthcare, there are concerns regarding medical ethics in AI and concerns over AI partly replacing doctors. These are legitimate concerns and they highlight the challenges and need for further examination and discussion on the ethical, social and economic implications of AI in healthcare. By overcoming these challenges, AI has the potential to greatly benefit health care providers, healthcare systems and patients alike.
Overall, the application of AI in healthcare shows great promise for improving disease detection and diagnosis, as well as enhancing the efficiency and effectiveness of healthcare systems. It is expected to continue to be of great interest around the globe in the coming years. However, it also presents challenges that must be addressed.
Future direction should focus on developing robust and reliable AI models, addressing barriers and challenges and ensuring the ethical and responsible use of AI in healthcare.
Dr. Man Hung is the senior associate dean for research and graduate education at Roseman University of Health Sciences College of Dental Medicine, and adjunct faculty at the University of Utah School of Medicine. She specializes in artificial intelligence, global business management and leadership.