4 Ways AI is Used in Healthcare Now

The AI in healthcare market was valued at $8.23 billion in 2020 and is projected to reach $194.4 billion by 2030, growing at a CAGR of 38.1% from 2021 to 2030.

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Healthcare is experiencing a wave of technological innovations that are transforming the industry. AI applications in health tech include self-care apps, symptom checkers, e-triage AI tools, virtual assistants, bionic pancreas in diabetic patients and robot-assisted surgery.

AI solutions have also made their way into managing hospital operations, like optimizing scheduling or bed management. And predictive analytics tools are helping detect the risk of hospital admission or helping detect specific cancers early and enabling early intervention.

Extended Intelligence

As health institutions accelerate the use of AI, they are honed in on two primary goals. One is to increase the potential for impact from using AI, and the other is improving the user experience.

Some of the important applications of AI in healthcare include:

Medical diagnostics

 

AI has been used in medical diagnostics for a while, but recently, a team led by scientists at the University of California and the University of Surrey used AI-powered networks to help identify, anticipate, and analyze some common symptoms of cancer patients undergoing chemotherapy.

 

This is a pioneering use of AI network analysis as a technique to evaluate the connection between common symptoms exhibited by a large group of cancer patients receiving chemo treatment and has the potential to help zero in and predict cancer development with more accuracy and earlier than any other technology.

Drug Discovery


The big ten pharma companies are all currently leveraging AI, including Novartis, Roche, Pfizer, Merck, AstraZeneca, GlaxoSmithKline, Sanofi, Abbvie, Bristol-Myers Squibb and Johnson & Johnson.

 

Some are developing their own AI solutions, while others are choosing to work with AI startups. Roche, for instance, has been on an acquisition spree, targeting later-stage and mature healthcare AI startups and has partnered with Owkin, Flatiron, Syapse and GNS Healthcare.

Clinical Trials

 

Most clinical trials are managed offline with no integrated solutions that can track progress, data gathering and drug trial outcomes. AI is not only making clinical trials more efficient, but it’s also helping bring more accuracy to the outcomes by using data from patients who are more suited to a study.

 

For example, Emblema, a two-sided marketplace, allows patients to sell their medical data to life sciences organizations looking for drug trial participants. Unlike traditional media companies, which simply recruit patients for medical trials, it helps collect and enrich medical patient profiles for pharma companies to achieve better medical trial results and outcomes.

 

The company uses AI, IoT and blockchain to maintain the integrity and accuracy of the trials. Pharma companies can find more qualified patients faster for their medical trials, and they can get better patient profiles to submit with their trials for FDA approval.

Pain Management

 

AI in pain management is an emergent area in health tech, one with the potential to understand the nuances of human pain.

 

Currently, a study is being led by researchers in Northwestern University that’s using applied artificial intelligence or machine learning algorithms to analyze physiological information such as respiratory rate, oxygen levels, pulse rate, body temperature, blood pressure from patients suffering from chronic pain from sickle cell illness.

 

Using this information, they created models that could derive pain levels and identify changes in pain levels through machine learning. Thus far, the algorithm was able to beat baseline models to gauge subjective pain levels and distinguish between changes in pain and abnormal pain fluctuations.

 

Outside of this research, companies like PainCheck are using AI to assess pain in patients with dementia, who find it very difficult to communicate. The solution uses automated facial-analysis technology and smart automation to allow healthcare professionals to look for pain when it’s not evident, evaluate its intensity and optimize the treatment.

 

Instead of supplanting human doctors, AI in healthcare will empower them, helping them deliver better care.

Some of the important applications of AI in healthcare include:

Medical diagnostics

 

AI has been used in medical diagnostics for a while, but recently, a team led by scientists at the University of California and the University of Surrey used AI-powered networks to help identify, anticipate, and analyze some common symptoms of cancer patients undergoing chemotherapy.

 

This is a pioneering use of AI network analysis as a technique to evaluate the connection between common symptoms exhibited by a large group of cancer patients receiving chemo treatment and has the potential to help zero in and predict cancer development with more accuracy and earlier than any other technology.

Drug Discovery


The big ten pharma companies are all currently leveraging AI, including Novartis, Roche, Pfizer, Merck, AstraZeneca, GlaxoSmithKline, Sanofi, Abbvie, Bristol-Myers Squibb and Johnson & Johnson.

 

Some are developing their own AI solutions, while others are choosing to work with AI startups. Roche, for instance, has been on an acquisition spree, targeting later-stage and mature healthcare AI startups and has partnered with Owkin, Flatiron, Syapse and GNS Healthcare.

Clinical Trials

 

Most clinical trials are managed offline with no integrated solutions that can track progress, data gathering and drug trial outcomes. AI is not only making clinical trials more efficient, but it’s also helping bring more accuracy to the outcomes by using data from patients who are more suited to a study.

 

For example, Emblema, a two-sided marketplace, allows patients to sell their medical data to life sciences organizations looking for drug trial participants. Unlike traditional media companies, which simply recruit patients for medical trials, it helps collect and enrich medical patient profiles for pharma companies to achieve better medical trial results and outcomes.

 

The company uses AI, IoT and blockchain to maintain the integrity and accuracy of the trials. Pharma companies can find more qualified patients faster for their medical trials, and they can get better patient profiles to submit with their trials for FDA approval.

 

Pain Management

 

AI in pain management is an emergent area in health tech, one with the potential to understand the nuances of human pain.

 

Currently, a study is being led by researchers in Northwestern University that’s using applied artificial intelligence or machine learning algorithms to analyze physiological information such as respiratory rate, oxygen levels, pulse rate, body temperature, blood pressure from patients suffering from chronic pain from sickle cell illness.

 

Using this information, they created models that could derive pain levels and identify changes in pain levels through machine learning. Thus far, the algorithm was able to beat baseline models to gauge subjective pain levels and distinguish between changes in pain and abnormal pain fluctuations.

 

Outside of this research, companies like PainCheck are using AI to assess pain in patients with dementia, who find it very difficult to communicate. The solution uses automated facial-analysis technology and smart automation to allow healthcare professionals to look for pain when it’s not evident, evaluate its intensity and optimize the treatment.

 

Instead of supplanting human doctors, AI in healthcare will empower them, helping them deliver better care.

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