April 24, 2024

Slow and steady introduction of genAI into APAC healthcare by 2024

Never has a modern tool promised more efficiency and support than what generative AI delivers. Last year, sectors including healthcare quickly responded to the trend to be the first to benefit from the lead. The applications are also varied, from documentation to clinical decision support and even in combination with robotics.

While the Enthusiasm for genAI will remain high this year – with governments investing even in this emerging lucrative space, the pace of adoption is likely to be slow, says Kota Kubo, CEO of Ubie.

We interviewed him to discuss how their partners in Asia Pacific and the United States have received genAI, where this trend may go in 2024, and how healthcare stakeholders can effectively leverage the tremendous value of genAI.

Q: How is generative AI being received and benefited by your partners?

A: Our partners in Japan, especially the more innovative ones, are generally interested in genAI because they understand that it can impact the efficiency of their work. They are interested in using a genAI platform from ChatGPT or Med-PaLM in their clinics, but most of them do not have access to the internet – unless they introduce cloud-based services like Ubie.

Even if they could use genAI successfully, only the more tech-savvy users would be able to take full advantage of it because it is difficult for users to come up with actual use cases. So there is now market demand for software vendors to provide use cases that combine genAI and can be integrated into the actual clinical workflow.

We have seen successful adoption and integration of genAI into our product, which is now being used in clinics and hospitals. We recently released a feature that uses LLM (large language model) to summarize patient symptoms and free-response responses. It gives doctors quick insight into a patient’s condition so they can have more face-to-face time with patients. During pilot testing 90% of physicians said they planned to continue using the feature. This shows that if a solution is accessible and has a positive impact on physicians, it will be adopted.

There still are barriers to full adoption, such as systems, safety and work habits, but there is already strong adoption of Ubie: we have now reached 47 prefectures in Japan and more than 1,700 medical institutions.

Q: Do you see any difference, for example by culture, with the way genAI is received in Japan versus the US?

A: Yes, there are big differences. Some are cultural in nature, but many are due to healthcare systems. Two important factors are that Japan’s statutory health insurance system provides universal coverage and that patient data is managed more centrally in Japan than in the US.

With universal coverage, Japanese people are encouraged to go to the doctor when they feel unwell. In the US, the decision to see a doctor is heavily influenced by insurance coverage, copays, and out-of-pocket costs. This typically causes American patients to avoid taking control of their health unless a condition becomes very serious. So reimbursement is an important consideration when looking at the US market.

From an AI perspective, the centralization of data in Japan makes AI deployment more scalable. Technology companies are accelerating their approach to accessing government data, which offers a large database. In Japan, one of the biggest hurdles to genAI adoption in hospitals is network, as the majority of hospitals do not have internet access.

Overall, AI in healthcare is less busy in Japan than in the US, meaning decision makers have less noise and clutter to cut through. However, this limits options and may delay the adoption of more advanced technologies.

Q: Where do you think generative AI adoption in healthcare in Asia and the US will go this year, 2024 and beyond? Will the hype continue?

A: The hype will continue well beyond 2024, but adoption will be slow. That’s because genAI continues to prove it can have a safe and effective impact in every area of ​​healthcare. We will exercise the utmost caution in anything that has a direct impact on patient care. We know technology can improve healthcare, but if it doesn’t function as expected, it can slow down healthcare systems, cost money and increase consumer loyalty, and worst of all, impact patient outcomes.

Across Asia and in the US, the most important aspects of adoption will be regulation cultural barriers. Developers must take into account how an AI engine functions within local laws and regulations. Privacy will be one of the first major areas a company must address, followed by adapting to each individual healthcare system – direct to patients, hospital systems, pharmaceuticals – and of course, patient rights.

From a cultural perspective, there are many. One challenge Ubie faced was how to adapt different cultural contexts. While many LLMs have strong translation capabilities, each place has different preferences and needs. Ubie experienced this first-hand when we launched the platform in Singapore and the US. In addition to customizing instant translations, we needed to tailor our user interface to patient preferences, and the only way to do that is by listening to your users. So I expect that many technology players will focus on experimentation, data collection and customization in the coming year.

Finally, from a global perspective, it is important that we stop thinking of genAI as a monolith coming to save healthcare. It is more of a collection of different precision machines, each with specific capabilities. It is not a one-size-fits-all solution.

The success and confidence in the systems ultimately implemented will be based on real-world results and proven accuracy. Truly useful systems must consistently replicate or exceed current human standards, or increase efficiency.

Q: Where else in healthcare do you think genAI can add value in the future?

A: AI can go anywhere as long as we have the ability to dream and develop it. We’re already seeing AI impacting clinical trials, documentation, patient interactions and more. GenAI can also assist with data analytics to help democratize insight generation and research. However, bringing value and success will largely depend on the knowledge of developers and the willingness of users.

System capabilities and intelligence will be the differentiator. When you look at an engine, for example ChatGPT, your product needs to be more than just a skin on top. Only those with a deep understanding of the technology and their specific healthcare area can truly harness the potential of genAI.

Developers should also ask themselves how well their machine learning adapts to new input and how accurate the AI ​​is in implementing appropriate changes. Is your model brittle, does it have an appropriate feedback loop, are you able to quickly and easily implement new rules and changes in the larger healthcare system?

From a user perspective, change management will be critical, because without internal acceptance it doesn’t matter what the tool can do. Then you need to ensure that staff actually use the systems. Many tools have limited impact because no one has the time, patience, or energy to learn or use them. This is another area where companies will need to consider cultural differences and attitudes towards new technologies as they enter new markets.

Regardless of what potential adoption systems exist, the true measure will be how this helps patients and how you will gain the trust of the healthcare system, providers and patients.


Responses have been edited for brevity and clarity.

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