How AI and Machine Learning Are Changing The Health-Care Industry?

Healthcare is a recession-proof industry. The healthcare industry can withstand economic downturns and financial turmoil because humanity will always want healthcare. Indeed, during the Great Depression in the United States, when the economy was in a slump, the healthcare industry grew by 852,000 jobs.

Healthcare AI is expected to be worth $6.6 billion in the United States by 2022.

From clinical trials to new medication research and development, and from novel medical devices to nanoparticle technologies, AI and machine learning have touched every point and have the potential to totally revolutionize them.

According to a study conducted by Accenture, AI applications in healthcare might result in global savings of $150 billion by 2026.

If AI solutions is deployed correctly, the possibilities are boundless and the outcomes unimaginable.

Here are a few examples of how AI and machine learning might affect the healthcare industry:

The Iron Triangle's Solution

The triangle tries to tackle a basic healthcare problem: that of good quality, accessible treatment at a reasonable cost, which has plagued the world for many years.

Because healthcare is usually expensive, providing all three at the same time is a huge difficulty. Trying to improve one component has the unintended consequence of harming another.

However, by changing the current healthcare cost structure, AI can tackle this problem in the near future without breaking the triangle. The key is AI and smart machines, which the patient may utilise for self-treatment the majority of the time, lowering treatment costs and improving quality of life by minimizing human contact.

Imaging And Diagnostics

The US Food and Drug Administration has significantly boosted its investment in AI in imaging and diagnostics. And there's a good reason behind it.

The IDx-DR was the first AI system to be approved by the US Food and Drug Administration to make diagnostic choices. Early detection of mild diabetic retinopathy was a game-changing discovery. The device was 87.5 percent accurate, and it correctly diagnosed people who didn't have the illness up to 89.5 percent of the time.

The Viz.AI, a form of clinical decision support system that analyses CT scan results to identify the possibility of a stroke in patients and sends the results to a professional to identify any block, was also approved by the US FDA.

Diagnostics, in fact, is quickly becoming one of the most important drivers of AI investment in healthcare.

Screening At An Early Age

Early detection of most diseases can reduce patient mortality rates by more than half and reduce treatment expenses by more than half.

Consider the case of colorectal cancer.

Stage 1 CRC has a 5-year survival rate of roughly 90%, compared to only 10% for Stage 4 CRC.

CRC can be detected early and treated using a minimally invasive endoscopy that costs less than $5,000 per year. Late-stage CRC, on the other hand, necessitates multidisciplinary treatment, including several surgeries, chemotherapy, and radiation, which drives up expenditures.

That is why early detection is so important, and AI services can help with that. This is already possible with apps available on the market. Autism & Beyond, for example, is a groundbreaking app that used Apple's Research Kit to collect videos of children and use AI technologies to predict their preference for the development of autism.

AI deployed for early screening can save billions of dollars in taxpayer money each year and dramatically reduce out-of-pocket spending in the United States.

Drug Development And Research

According to the California Biomedical Research Association, a medicine takes around 12 years to develop in the lab and reach the patient.

Only 1 out of every 5000 medications chosen for pre-clinical testing is employed in human trials, and only 20% of those treatments make it to the market for human use.

A new medication currently costs more than $2.5 billion to develop.

AI has just lately been applied in drug research and development. The power of artificial intelligence (AI) can be used to speed up the drug discovery and repurposing processes. It can identify patients who are most likely to benefit from the study, as well as those who are in desperate need of new treatments.

For starters, all of them can lead to considerably safer clinical studies with fewer adverse medication reactions.

And then there's the issue of cost-cutting. In fact, according to a study conducted by Carnegie Mellon Institution and a German university, AI solutions might reduce drug discovery costs by up to 70%.

This, in turn, will be passed on to patients in the form of decreased drug prices, increasing patient access to better drugs and improving overall population health.

Surgery

In the United States, AI-enabled robotic-assisted surgeries are sweeping the country. They're becoming more popular as a way to reduce surgeon variability and increase quality.

'Artificial intelligence can aid surgeons in their work.' Dr. John Birkmeyer, a chief clinical officer at Sound Physicians, is quoted in the article.

Advanced analytics and machine learning techniques are being used in tandem to extract critical insights from the billions of data points generated by robotic surgery. This can assist overcome inefficiencies and enhance patient health outcomes if applied correctly.

Artificial intelligence assists surgeons in making better clinical judgments in real time during surgery and in comprehending the dynamics of the patient, particularly during difficult surgeries. It also cuts the length of time patients spend in the hospital by 21%.

This is evident in the patient's long-term health and post-operative care. It also reduces readmissions, saving millions of dollars every year.

When compared to surgeons working alone, AI-assisted robotic surgery resulted in five times fewer problems, according to a study involving 379 orthopedic patients.

Virtual Nurses Aided By AI

By 2026, AI-assisted virtual nurses might save the US healthcare business $20 billion each year.

They are available 24 hours a day, 7 days a week to answer any patient questions, monitor patients, and assist them in any way they require.

Currently, they serve as a conduit for information interchange between care providers (doctors) and care recipients (patients) in order to determine which medications to begin, current health condition, recent test results, and a variety of other factors.

It can save the patient a lot of doctor's appointments and lower hospital readmission rates by providing simple, engaging, and intelligent treatment.

Care Angel is one of the most skilled virtual nurses available. It can also provide wellness checks by speech and AI, in addition to all of the above.

Wrap-Up

In healthcare, AI and machine learning are still in their infancy. Adoption on a big scale has yet to occur. In order to be effective in the healthcare industry, AI and machine learning must have the support of healthcare professionals such as physicians and nurses.

However, a lot of money is being invested in AI in healthcare, and it's growing quickly.

Artificial intelligence in healthcare is now targeted at improving patient outcomes, balancing the interests of multiple stakeholders, enhancing accessibility, and lowering healthcare expenditures.

However, in the near future, AI and ML, as well as technologies like Data analytics solutions, will play a much more holistic role in advancing healthcare.