Artificial intelligence holds promise for use in any number of healthcare applications from monitoring to tracking patient responses to medications and dosages. AI holds the greatest promise, in my opinion, for optimizing complex treatments for patients that requiring "tuning" of their medications and other forms of treatments, while tracking their progress and determining what works best for the individual patient. All to often meds are prescribed that do not match fully the specific needs of a single patient. Drug efficacy trials and larger clinical studies point to benefits from using AI system and machine learning to arrive at optimized treatments corresponding to the response to the treatment.

It will not be for in the future where genetically modified treatments originating from CRISP research studies will be able to customize the specific best treatment for an individual based on the collated results of others.

As you mentioned it can help with fine tuning medication and medication compliance. Currently there are individualized treatments for cancer patients using specific genetic markers. Given the vast amount of genetic markers that may institutions have such as 123DNA etc. tapping into those and then being able to artificially customize treatments would in my opinion produce better results than the trial and error approach used in today's medicine.

Currently there is a company that is trying to use AI along with analytics to customize medication treatment for psychiatric patients leading to more productive lives and compliance.

Here is an article highlighting 12 possible applications of artificial intelligence and machine learning in the healthcare fields.

https://healthitanalytics.com/news/top-12-ways-artificial-intelligence-will-impact-healthcare

It should be noted that AI future applications coupled together with the networks of clinicians working with CRISPR and other treatment models hold great potential for improving the outcomes and quality of life and hold the promise of significantly reducing healthcare costs globally.

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