As artificial intelligence-assisted technologies are developing rapidly in areas such as the healthcare sector, university researchers are helping policy-makers to identify the gaps and barriers to rapid implementation.
As part of the Association of Pacific Rim Universities’ (APRU) AI for Social Good project, in collaboration with the United Nations Economic and Social Commission for Asia and the Pacific in Bangkok, university-based academics have been working with Thai policy-makers to assess gaps and bottlenecks in implementing AI in healthcare.
The academics then support the Thai government in developing policies to help build AI capabilities.
The two-year APRU project funded by Google, which has just ended, “aimed to work with government partners in Asia and the Pacific to grow sound and transparent AI ecosystems that support sustainable development goals”, explained APRU’s chief strategy officer, Christina Schönleber.
Research has already shown that AI can make healthcare more efficient, improve patient outcomes and support medical research. Newer AI such as voice-to-text and generative AI tools for summarising patient data have also proven useful for health workers in the field.
“For Thailand we were looking at barriers and enablers for data sharing for AI healthcare,” explained Jasper Tromp, assistant professor at the National University of Singapore and APRU’s research lead for the project.
“In addition to rigorous research, the Thai partners emphasised the need to be relevant to the Thai people, and they also saw the benefit of researchers coming from different regions, because they could bring knowledge from their own regions,” explained Toni Erskine, professor of international politics at the Australian National University (ANU) in Canberra, who was the research lead for the overall APRU AI for Public Good project.
For artificial intelligence to be useful in countries like Thailand, it is crucial that data can be shared. But many governments are unaware of the specific barriers or enablers for joined up data such as patient data or imaging data for healthcare, Tromp noted.
Limited data availability and varying data storage standards also pose significant challenges to AI development and deployment, the research found.
One of the aims of the APRU project, in collaboration with the Thai Office of National Higher Education Science Research and Innovation Policy Council, was “specifically to inform development of a guideline or protocol to enable data sharing between government institutions, but also between government institutions and private partners, such as companies or universities or external organisations that would use this type of data”, Tromp explained.
AI solutions for Thailand
Thailand is developing its AI capabilities to help bridge gaps in skills and healthcare coverage beyond major cities. But implementing AI-assisted healthcare still has significant hurdles to overcome, and many examples that resolve some of these have been developed in the United States or Europe.
“Many of these AI algorithms are trained in the US or Europe and most of the training data is derived from either white people or African American people and people that do not share the same ethnic background [as Thais], so they might not work as well in the Thai or Asian local context as they do in the context where they’re developed,” said Tromp.
“For both practical as well as economic reasons, Thailand is very eager to develop their own AI industry and apps that can be deployed locally,” he added. In part, this is because some of the AI-driven healthcare systems developed overseas are expensive to acquire and implement. Also, Thailand wants solutions geared to the local context.
Some research work on AI for medical applications has been ongoing within Thailand, with some companies expecting to release them on the market in the near future. “AI has shown a lot of promise in healthcare. It’s being used now in terms of chatbots, and it is being implemented for image recognition,” Tromp said.
What currently exists is fairly general. “But for health records for public health it has to be very high-level data.”