Why AI has yet to Solidify its Role in Healthcare?

AI has significantly assisted health care industry with applications ranging from detecting cancers to simple calorie intake monitor.

Artificial Intelligence (AI) is one among the emerging area of research with several domain applications. One such prime domain is Healthcare. Although, AI has significantly assisted healthcare industry with applications ranging from detecting cancers to simple calorie intake monitor.

Certainly, there exists a gap to be filled for AI being a major support to the health industry. There could be possibly a number of reasons for the gap from several domain aspects. Here are few top reasons for the same-


  1. Research regulations

Several AI literatures can be found in the repositories addressing healthcare problems. It can be noted that these literatures simulate healthcare problems and solutions with finite sample dataset. However, it is the fact that researches can be conducted only on sample dataset because of research regulations.

Regulations on human experimentations leave several significant research finding only on research papers rather than real-time experiments. Although these constraints are much needed, they lead to a vicious circle in AI research and its real-time applications.

Hence, assistive wearable products and adhesive products are successful than supporting implants.


  1. Money and convenience

Money plays a huge role in healthcare applications of AI. AI products come in different forms like mobile applications, smart wristband, smart clinical monitor or neural implants. External or adhesive products of AI are more affordable and convenient to try it on for people.


  1. Predictive analysis may not be reliable

Most AI applications provide predictive analysis. It could be noted that AI system will learn to predict based on the samples introduced to it. It is a general notion in research to introduce samples to the system with the help of dataset. Acquiring a dataset that could possibly cover all the variations is next to impossible.

So, AI systems are made more reliable as much as possible evolutionarily. A cancer detecting AI system could detect cancer with accuracy if the subject has symptoms that are already introduced to the system.


  1. Cost of Computational resources

AI systems that are running on decent configuration system reach people well than that runs on cluster or supercomputer. This, in turn, is the limitation of money as well as knowledge of computation. Simple disease predictive systems or health monitors work well with day to day computer.

However, a system detecting brain cancer would have run the subject with all templates in its database. This certainly eats computational power of the resource.


  1. Commoners are yet to be ready for next-generation treatment

Most commoners are used to drug prescription or surgeries for illness. A certain variety of AI devices like implants has to be placed inside the human body for illness correction or improvement. Alternatively, people are used to visiting physicians during illness. AI introduces trends such as virtual assistance of patients to monitor and treat patients.

Certain AI devices like smart band record the activity of the patients including movements, sleep, calorie intake and report to physicians over online for suggestive changes in their activities. People are just moving towards such technologies.

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