Health Care AI Trends In 2023
Connect with us


Health Care AI Trends in 2023



Health Care AI Trends in 2023

(CTN News) – As the health care industry embraced AI slowly at first, it will rapidly increase its adoption over the next few years. This is due to medical image analysis and drug discovery being the most popular applications, according to Andrew Brosnan, principal analyst at Omdia, a sister research firm.

The health care industry will spend nearly $6.2 billion on AI software in 2023, up from $4.4 billion in 2022, according to Omdia. In 2027, spending on AI in the health care sector will rank second only to consumer spending, according to Omdia’s AI & Intelligent Automation Practice director Brosnan.

AI adoption in healthcare will catch up.

The high stakes of patient care and privacy, security, and regulatory concerns have historically prevented health care companies from adopting the latest technologies. AI adoption in healthcare is lagging. Only 19% of health care organizations have scaled AI across multiple business units or functions, according to an Omdia survey.

But that’s changing fast. Health care has proven AI to be effective, which is why it is growing in use. Health care providers used AI during the pandemic to diagnose COVID-19, assess patient prognosis, and understand spike protein variations.

AI’s use in the pandemic and proof-of-concept projects bolsters confidence in its potential in health care, Brosnan said.

96% of health care organizations surveyed by Omdia in 2022 said they are confident or very confident that AI will deliver positive results. In addition, 67% said AI had added value over the past year.

Massive AI investments will assist in intelligent document processing, and AI software spending will grow at a compound annual growth rate (CAGR) of 29% to $13.8 billion in 2027, tied for the fastest growing sector.

Use cases for health IT.

AI’s most popular use case is medical image analysis. It will retain the lion’s share of spending, with $2.6 billion in AI software spending in 2027. According to Omdia forecasts, drug discovery will reach $2 billion in AI spending by 2027, growing at a 33% CAGR.

Other top use cases are online chatbots and intelligent document processing, both with a 27% CAGR. Virtual assistant spending is expected to reach nearly $1.7 billion in 2027, while intelligent document processing will hit $1 billion.

In 2027, AI spending will reach $900 million for medical treatment recommendations – through tools like clinical decision support.

Drug discovery revolutionized

AI has the potential to accelerate and reduce the cost of drug discovery and development. According to Brosnan, the pharmaceutical industry will continue to advance drug discovery through AI in 2023.

To bring a brand-new drug to market, it takes about $1 billion and 10 years. To advance a candidate to clinical trials, more than 5,000 molecules are synthesized.

Drugmakers can reduce the number of molecules they need to physically create by doing it “in silico,” meaning virtually.

Thus, only 250 molecules need to be synthesized, saving money and reducing time to market, Brosnan said. In 2022, 18 AI first-drug candidates will be in clinical trials. There were none in 2020.

The process takes months, if not years, of early drug discovery.

Health Care AI Model Training with Emerging Technology

According to Brosnan, federated learning or swarm learning, which enables health care providers to securely use patient data to train AI models, will gain traction in 2023. AI models must be trained against a large dataset to reduce bias. However, to train an AI model, many health care institutions would like to share data.

The data would traditionally be stored in one central repository. The data does not have to move with federated or swarm learning. The AI model is trained on data from each facility individually, he said. By doing so, health care providers can ensure their data is secure and governed.

Data does not need to leave the source institution with federated or swarm learning, Brosnan said. Swarm learning uses a distributed orchestrator, rather than a centralized orchestrator in federated learning.

A proof-of-concept is currently underway. An investment of $180 million was made by Sanofi in 2021 in a health care federated learning company.


False Claim: Pfizer Vaccines Cause Blood Clots, Says FDA

Continue Reading

CTN News App

CTN News App

české casino

Recent News


compras monedas fc 24

Volunteering at Soi Dog

Find a Job

Jooble jobs

Free ibomma Movies

Exit mobile version