Artificial Intelligence Trends in Healthcare in 2023

Andrew Brosnan, principal analyst at research firm Omdia, predicts that although the healthcare industry is initially slow to embrace artificial intelligence, healthcare and pharmaceutical companies will increase it rapidly in the next few years. Adoption of artificial intelligence, medical image analysis and drug discovery will be the most popular use cases.

Healthcare spending on AI software is expected to grow 40% in 2023, from $4.4 billion in 2022 to nearly $6.2 billion in the new year, according to Omdia forecasts.

"Healthcare will grow faster than most other industries, and according to our forecasts, we expect healthcare to rank second behind consumers in AI spending in 2027," Omdia Artificial Intelligence and Brosnan of the Intelligent Automation practice said.

Healthcare AI adoption will catch up with other industries

Brosnan said healthcare companies have historically been conservative in adopting new technologies because of risks to patient care and privacy, security and regulatory issues. Very high.

Healthcare lags behind other industries in artificial intelligence adoption. According to a 2022 OM Dia survey, while 25% of all industries have expanded AI deployment across multiple business units or functions, only 19% have done so in healthcare.

But this is changing rapidly. Artificial intelligence has proven effective in health care, which has fueled growth in use, he said. For example, artificial intelligence has been used during the epidemic to help health care providers with COVID-19 diagnosis, patient prognosis, and to help researchers understand changes in the spike protein.

“The use of AI during the pandemic and in proof-of-concept projects has increased confidence in the value AI can provide in healthcare,” said Brosnan.

In fact, 96% of healthcare organizations surveyed by Omdia in 2022 said they are confident or very confident that artificial intelligence will lead to positive outcomes, and 67% of respondents said that artificial intelligence will increase The ability to value has increased over the past year.

This will translate into significant investment in artificial intelligence. Spending on AI software will grow at a compound annual growth rate (CAGR) of 29% and reach $13.8 billion in spending in 2027, tied for the fastest-growing sector, according to Omdia.

Top Five Healthcare IT Use Cases

Medical image analysis is the most popular use case of artificial intelligence. With annual growth of 26%, it will retain the largest share of spending, reaching $2.6 billion in AI software spending in 2027.

Meanwhile, Omdia forecasts show that drug discovery will be the fastest-growing use case by 2027, with AI spending reaching $2 billion and a CAGR of 33%.

Other top use cases are virtual assistants such as online chatbots and intelligent document processing, both of which have a CAGR of 27%. AI spending for virtual assistants is expected to reach nearly $1.7 billion in 2027, while intelligent document processing (such as claims processing) is expected to reach $1 billion.

Medical advice – through tools such as clinical decision support – completes the top five use cases with a CAGR of 28% and $900 million in AI spending in 2027.

Innovating Drug Discovery

Brosnan said that artificial intelligence has the potential to speed up the drug discovery and development process and reduce its cost. In 2023, the pharmaceutical industry will continue to advance drug discovery through artificial intelligence.

The traditional drug discovery and development process currently takes approximately $1 billion and 10 years to bring a new drug to market. This involves synthesizing more than 5,000 molecules to advance a candidate into clinical trials, he said.

But with artificial intelligence, drugmakers can reduce the number of molecules they have to physically make by doing production "on the computer," meaning they can do it virtually, he said.

This reduced the number of molecules they had to physically synthesize to 250, which saved money and shortened time to market, Brosnan said. The pipeline of AI-first drug candidates is very strong, with 18 candidates entering clinical trials in 2022. In 2020, that number was zero.

"Early-stage drug discovery takes months or even years," he said.

Emerging technologies could better train healthcare AI models

Federated learning, or group learning, is an emerging technology that will allow healthcare providers to safely use it with patients, Brosnan said. data to better train AI models and will gain greater traction in 2023.

To reduce bias, it is important to train AI models against large data sets. But to do that, many healthcare organizations want to share data so they can build a more comprehensive dataset to train AI models.

Traditionally, they had to move data to a central repository. However, with federated or group learning, the data does not have to move. Instead, he said, the AI ​​model goes to each individual health care facility and is trained on the data. In this way, healthcare providers can maintain the security and governance of their data.

“With federated or swarm learning, the data does not have to leave the source institution, but the AI ​​model moves to the data,” Brosnan said. Federated learning uses a centralized orchestrator, while swarm learning is more distributed and does not use a centralized orchestrator.

This technology is currently undergoing a proof of concept. In 2021, major pharmaceutical company Sanofi invested $180 million in a healthcare-focused federated learning company.

"This is an emerging technology and we will see the rise of it in 2023 and 2024," he said.

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