Artificial intelligence (AI) is a collection of computer algorithms displaying aspects of human-like intelligence for solving specific tasks. Another synonymous term, augmented intelligence is a conceptualization of artificial intelligence that focuses on AI’s assistive role, emphasizing that its design enhances human intelligence rather than replaces it.
Although data-driven care is a cornerstone of modern medicine, data-driven decision making can be complicated and fraught with error. Similarly, although AI tools can transform the practice of modern medicine in many beneficial ways, clinical decision support based on AI output without a basic understanding of AI technology can have serious, even fatal consequences for patients. It is therefore important to note that when this technology is used for clinical decision making, it should be used more responsibly and ethically.
Extensive research is necessary to assess the short and long-term risks and effects of the clinical use of AI on quality of care, health disparities, patient safety, health care costs, administrative burden, and physician well-being and burnout. It is critical to increase overall awareness of the clinical risks and ethical implications of using AI, including any measures that can be taken to mitigate the risks.
Comprehensive educational resources are necessary to help clinicians, both in practice and in training, navigate this rapidly evolving area of technology, including improving their collective understanding of where the technology may be integrated in systems they already use and recognizing its implications.
Developers of AI must be accountable for the performance of their models. There should be a coordinated federal AI strategy, built upon a unified governance framework. This strategy should involve governmental and nongovernmental regulatory entities to ensure the oversight of the development, deployment, and use of AI-enabled medical tools; the enforcement of existing and future AI-related policies and guidance; and mechanisms to enable and ensure the reporting of adverse events resulting from the use of AI. And, in all stages of development and use, AI tools should be designed to reduce physician and other clinician burden in support of patient care.
Use of AI in health care must be aligned with principles of medical ethics, serving to enhance patient care, clinical decision making, the patient–physician relationship, and health care equity and justice. AI developers, implementers, and researchers should prioritize the privacy and confidentiality of patient and clinician data collected and used for AI model development and deployment.
Clinical safety and effectiveness, as well as health equity, must be a top priority for developers, implementers, researchers, and regulators of AI-enabled medical technology and that the use of AI in the provision of health care should be approached by using a continuous improvement process that includes a feedback mechanism. This necessarily includes end-user testing in diverse real-world clinical contexts, using real patient demographics, and peer-reviewed research. Special attention must be given to known and evolving risks that are associated with the use of AI in medicine
It is therefore necessary that training be provided at all levels of medical education to ensure that physicians have the knowledge and understanding necessary to practice in AI-enabled health care systems. Moreover, the environmental impacts of AI and their mitigation should be studied and considered throughout the AI cycle.
(Author is a health columnist and works at SMVD Narayana Super speciality Hospital Jammu. Feedback: [email protected])
