6 healthcare AI predictions for 2023

6 healthcare AI predictions for 2023 Kolawole Samuel Adebayo

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In 2022, healthcare AI funding dropped to its lowest level since the third quarter of 2020, according to a CBInsights report.

However, amid the economic downturn, 2022 has arguably been the year of AI innovation across several industries, including healthcare — which is why the sector was impacted less than others by the fall in global AI funding this year.

CBInsights’ report revealed healthcare AI funding decreased 20% from Q2, compared to fintech AI funding, which dropped 34%, and retail tech AI funding, which fell by nearly half.

As the new year beckons, AI experts are examining 2022’s trends to predict what to expect in 2023. VentureBeat spoke to several to get a sense of where healthcare AI might be heading:

1. Personalized healthcare will be an even greater focus

Several experts believe the coming year will usher in a greater drive for personalized healthcare, aided by increasing volumes of data in the industry. IDC predicts that “the global datasphere will grow from 33 zettabytes of data in 2018 to 175 zettabytes by 2025” — a fivefold increase from 2018’s figures. Morris Laster, medical investments partner at Israeli venture capital firm OurCrowd, told VentureBeat that this increase in data will put the spotlight on personalized healthcare.

“For AI systems to make accurate predictions and recommendations, they must be trained on large amounts of high-quality data,” said Laster.

While he admits that many healthcare datasets are incomplete, noisy or biased — which can lead to inaccurate or unreliable models — he said that organizations leveraging AI for healthcare can address the challenge by carefully curating and verifying the data used to train their models.

“This can involve cleaning and preprocessing the data to remove any errors or inconsistencies, as well as conducting quality checks to ensure that the data is representative and accurate,” he added.

Micha Breakstone, cofounder and CEO at Neuralight, believes AI’s range in healthcare will get increasingly personalized, so “specific treatment can be generated based on the patient’s profile, genetics, environment and lifestyle in order to optimize patient outcomes.”

Andy Thurai, VP and principal analyst at Constellation Research, told VentureBeat that precision medicine or individualized medicine is an area where AI can help.

Until now, he said, it was almost impossible to configure the treatment, medication combination and drug mixing that can work for a specific patient, because of the data involved to create this individualized profiling. But given today’s advancements in genomic profiling, he explained, AI can determine what treatments will work, and what won’t, based on an individual’s genetic profile, past treatment history or current medication history and develop a customizable treatment for every patient — rather than relying on a pharmacist or doctor.

“The availability of wearable medical devices, telehealth consultations and predictive diagnosis using collected data in real time can all lead to identifying the right person for treatment while taking unnecessary treatments out of the equation, which can free up the doctor’s and other healthcare professionals’ time to [spend] where it is needed,” Thurai said. “In addition, remote health monitoring is easier now with wearable devices and AI escalating the issue only when necessary to schedule an immediate appointment.”

2. Legislation and regulations around healthcare AI will improve

The healthcare industry creates a vast amount of data. One report by IDC estimated that the industry “created 2,000 exabytes of data in 2020 and will continue to grow at a 48% rate year over year.”

This opens up unique data challenges in the healthcare industry, especially around data privacy. McKinsey debates the extent to which regulations will affect approaches to ethics, health data and patient confidentiality, and whether legislation will help the AI sector in the same way the General Data Protection Regulation (GDPR) has aided privacy protection.

But Ayanna Charles, solutions consultant at predictive software company Verikai, believes that legislation and regulations will get better next year. “In the short term, legislative and regulatory bodies at both the state and national levels are getting more aggressive in their oversight of data sharing and the use of AI in healthcare,” she said. “Large insurers and healthcare providers are inherently conservative, so we expect more guidance regarding what constitutes acceptable use of data and AI.”

Charles added that the data produced will keep growing in the long term, making the value of AI greater. It will also prompt governments, industry and advocacy groups to “consolidate around a common framework and set of practices that balance the need to protect individuals’ data with the real medical benefits of using that data within AI models,” she explained.

3. There will be more efforts to tackle AI bias

While every industry using AI must address the big issue of bias in their models, a study by Harvard notes that it is particularly essential for healthcare providers to tackle AI bias head-on.

“Biases in healthcare AI can further worsen social inequalities and can cause death,” according to the study.

But Charles noted that 2023 will see the beginning of a reduction in biases for AI in healthcare.

“Anybody using AI in healthcare must consider the bias that exists within the health system as it exists today before building and deploying any models,” she said. “At worst, they may blindly build and deploy a model that propagates and reinforces existing bias. At best, though, AI models can be used to reduce and remove discrepancies within the health system.”

To address ethical concerns, organizations using AI for healthcare can implement guidelines and oversight mechanisms to ensure that their AI systems are being used responsibly and in compliance with relevant regulations, Lester explained.

Svetlana Sicular, research VP at Gartner, added that “responsible AI helps achieve fairness, even though biases are baked into the data; gain trust, although transparency and explainability methods are evolving; and ensure regulatory compliance, while grappling with AI’s probabilistic nature.”

4. A wider range of applications in healthcare

Accenture estimates that AI applications will cut annual U.S. healthcare costs by $150 billion in 2026. These savings are expected result from a wider range of applications. Taking a long-term look at the healthcare industry, OurCrowd medical analyst Tzvi Bessler said AI will continue to play an increasingly important role in healthcare over the next five years — beginning next year.

“This will likely involve the development of more sophisticated and intelligent AI systems that can make more accurate and reliable predictions and recommendations,” said Bessler.

Laster agrees: “In 2023, expect to continue to see AI being used more extensively for tasks such as drug discovery and development, and for improving the efficiency and accuracy of medical research.”

Thurai said he also believes AI will greatly help drug and treatment discovery.

“This has already started happening and there will be more next year,” he said.

He added that, “in a situation like the recent COVID-19 pandemic, given the enormity of affected patients and the speed at which the virus spread, the drug and vaccination discovery and experimentation in clinical trials cannot take the normal lifecycle.”

On average, he explained, a vaccination “can take years to bring to the market, but the COVID vaccine was mass-produced in billions and saved many lives using AI-assisted features such as discovery, experimentation, side effect, and efficiency tracking and so on — which would have been impossible to track using normal methods.”

5. A closer working relationship between humans and AI

While some tout that AI will replace humans across industries, Charles doesn’t see that happening in the healthcare space, at least in the next year.

Instead, she foresees a closer nexus between humans and machines.

“As with all new technologies, AI technologies should not be treated as the single solution to the business problem facing an organization,” she explained. “Implementing any AI solution requires technologists to establish and maintain a sound governance structure — one that marries the solution with human oversight, consistent policies and solid procedures.”

In fact, Gartner expects that “by 2023, all personnel hired for AI development and training work will have to demonstrate expertise in responsible AI, heralding a closer relationship between humans and machines.”

Thurai added that there must be easy and practical mechanisms for overriding AI decisions.

“Many managers and executives already working with AI admit they have had to intervene in their systems due to delivery of erroneous or unfair results,” said Thurai. “As more experts emphasize the need for a better human-AI relationship, we will begin to see organizations prioritizing this next year, especially in an industry like healthcare.”

6. Automation, automation and more automation

According to Laster, 2023 will be the year of more automation in healthcare.

He noted that we can expect to see AI being used more extensively for tasks such as managing patient records, scheduling appointments and coordinating care.

“This could help improve the efficiency and effectiveness of healthcare delivery and could also provide patients with access to more personalized and convenient care,” he said.

A recent analysis by McKinsey suggests that “AI-enabled personal assistants can automate 50 to 75% of manual tasks, boosting efficiency, reducing costs and freeing clinicians to focus on complex cases and actual care delivery and coordination. This, in turn, may improve the healthcare experience for both clinicians and insurance plan members.”

A promising future for healthcare AI

While Thurai concedes that it will take some time until AI systems can reflect the empathy that steers many human decisions, that doesn’t mean such systems shouldn’t be continuously improved to better mimic human values.

“AI only reflects the programming and data that goes into it, and business leaders need to be aware that cold, data-driven insights are only part of the total decision-making process,” he said.

Undoubtedly, there will be increased use of AI in healthcare next year, as more applications come to the fore and organizations begin to prioritize responsible AI.

In Laster’s words, “overall, the future of AI in healthcare looks promising.”

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