Recent developments in AI techniques are sending vast waves of development of technology across the healthcare ecosystem; there is an active discussion on whether AI enabled robots that can even acts as doctors will eventually replace human physicians in the future. However, human physicians cannot be replaced completely by machines in future instead AI can assist physicians to make better clinical decisions and can replace human judgment in certain functional areas of healthcare like radiology, diagnosis and so on. The current state of AI in healthcare is mostly in the pilot phase along with AI researches and programs that have developed in the meantime.
The applications of AI in medicine have mainly two branches:
- Virtual branch: The virtual component is represented by Machine Learning, which in itself is the AI’s application that automatically improves learning through experience. This approach takes control of health management systems including electronic health records, and ultimately guides the doctors in their treatment plan and clinical decisions.
- Physical branch: It includes physical objects, medical devices, and sophisticated robots for healthcare purpose, robots for surgery and so on.
The current demand to have an efficient healthcare system with more facilities than ever, has propelled the drivers of AI in healthcare. For instance, shortage of skilled personnel such as nurses and doctors which creates the place for virtual assistance and robot surgeons; need for the early detection and diagnosis of diseases; and for precision in medical treatment have altogether lead IBM’s AI platform Watson Analytics to expand its field in healthcare. The other factors that are influencing AI market to expand further in healthcare includes increasing individual healthcare expenses, growing geriatric population, rising global healthcare expenditure and so on. With every need to have a more advanced healthcare system, comes an innovative solution, which is AI in the case of healthcare.
Although, AI is attracting considerable attention in medical research, the real-life implementation still has some obstacles to overcome. The very first hurdle comes in the form of drawbacks of standards in current laws to assess the safety and efficacy of AI systems along with the data privacy issues. Another hurdle is data exchange. In order to keep working well, AI systems need to be provided with data continuously from clinical studies. However, once an AI system gets deployed on work after initial setting with the data available, continuation of the data supply becomes an issue for further development and improvement of the system. Current healthcare environment does not provide any encouragement for sharing data on the system. These drawbacks need to be addressed appropriately in order to augment the role of AI in healthcare.
Transforming piece by piece
Building an artificially intelligent healthcare ecosystem is going to be a great challenge due to the substantial burden of critical work on doctors, outdated systems and handwritten records, and old set of distribution between healthcare facilities. But as it is said, Rome wasn’t built in a day; good things do take time, hence, considerable progress has been observed in the healthcare system that does not disrupt the existing work of doctors and nurses already on service for patients while still offering improved care to patients.
The AI healthcare sector is full-fledged for development and investment. But, while the big data companies are trying to figure out how to transform the healthcare system; smaller-scale projects are making real changes. Piece by piece, patient by patient, AI is on its way to fix healthcare system flaws and needs all at once.
