Artificial intelligence (AI) in medicine is a problem-solving computerised program. It is like science fiction, something out of Star Trek. AI, robotics and big data are the future of medicine. It is expected to revolutionize patient care.
AI has capacity of a machine or computer to mimic intelligent human thought processes and learn new information. All this without being programmed to do so. This is just a simplified explanation. It is more complicated than that. But we will leave that for the experts to figure it out.
What I am interested to know is how it will affect the practice of medicine and patient care in the future. There is a lot of material out there on this subject. I have tried to make it as simple as I could.
Reviewing some articles in the medical journals indicate AI will impact on physicians’ ability to deliver quality care. Is it going to be good or bad? The view is there are a number of unknowns about AI, both in healthcare and more generally, which creates uncertainty for both physicians and patients.
There is no need to panic. We are still in the early stages for AI to be used in healthcare. Hopefully, there is enough time to ensure legal and regulatory frameworks are in place. By that time, we should know what AI can and cannot do, and not lose sight of the important goal of better patient care.
Advantages of using AI in health care
- AI is expected to improve healthcare and change the way it is delivered. It is expected to increase diagnostic accuracy, improving treatment planning, and forecasting outcomes of care.
- AI has shown particular promise for clinical application in image-intensive fields, including radiology, pathology, ophthalmology, dermatology, and image-guided surgery.
- AI technologies are currently intended to complement clinical care. It is too early for AI to take over clinical care.
- What distinguishes AI technology from traditional technologies in health care is the ability to gain information, process it and give a well-defined output to the end-user.
Challenges of using AI in health care
- Suicide prediction models have largely been ineffective to date.
- Evidence about the effectiveness and reliability of the practical applications of AI continues to be limited.
- AI is unabile to explain its reasoning processes, otherwise known as the “black box” effect.
- The use of AI in patient care can be limited in some situations when the AI-assisted diagnosis does not include information to verify its reliability.
- The dataset used by some has the potential to introduce bias. For example, a dataset that unintentionally excludes patients with certain backgrounds, conditions, or characteristics may not be reliable for broader segments of the population.
- There is no legal and/or regulatory clarity for physicians to use AI. The buck stops with the physician, not the machine.
The future of AI
The future is exciting. In the last half-century, the medical and technological advances have enabled the growth of healthcare-related applications of AI.
Medical institutions such as The Mayo Clinic, Memorial Sloan Kettering Cancer Center, Massachusetts General Hospital, and National Health Service, have developed AI algorithms for their departments. Some hospitals are looking for AI.
Medical community would like to see development of artificial intelligence systems for health care, which have the potential to transform the diagnosis and treatment of diseases, helping to ensure that patients get the right treatment at the right time.
The role of AI can help manage and analyze data, make decisions, and conduct conversations, so it is destined to drastically change clinicians’ roles and everyday practices.
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