As artificial intelligence-assisted technologies rapidly develop in areas such as the health sector, university researchers are helping policymakers identify gaps and barriers to faster implementation.
As part of the Association of Pacific Rim Universities (APRU). AI for Social Welfare The project, in partnership with the United Nations Economic and Social Commission for Asia and the Pacific in Bangkok, has university-based academics working with Thai policymakers to assess gaps and obstacles in the implementation of AI in health care.
Academics are supporting the Thai government in developing policies to help build AI capabilities.
The two-year APRU project, funded by Google, „aims to work with government partners in Asia and the Pacific to foster sound and transparent AI ecosystems that support the Sustainable Development Goals,” explained Christina Schönleber, APRU's chief strategy officer.
Research has already shown that AI can make healthcare more efficient, improve patient outcomes and support clinical research. New AI tools such as voice-to-text and AI tools for summarizing patient data have also proven useful for healthcare workers in the field.
„For Thailand, we were looking at the barriers and enablers of 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 emphasized the need to be relevant to the Thai people, and they also saw the benefit of researchers coming from different regions as they could bring knowledge from their own regions,” explained Professor Tony Erskine. In International Politics at the Australian National University (ANU) in Canberra, he was Research Chair for the overall APRU AI for Public Interest Program.
For artificial intelligence to be effective in countries like Thailand, it is important that data is shared. But many governments are unaware of specific restrictions or associated data, such as patient data or healthcare imaging data, Trump noted.
Limited data availability and varying data storage standards also pose significant challenges to AI development and deployment, the research found.
One of the goals of the APRU project, in partnership with the parent office of the National Higher Education Science Research and Innovation Policy Council, is to “inform the development of guidance or protocol for implementing data sharing, particularly among government agencies, but also among government agencies. And private partners like companies or universities or outside organizations that use this kind of data”, Trump explained.
AI Solutions for Thailand
Thailand is developing its AI capabilities to help bridge gaps in skills and healthcare beyond the big cities. But there are still significant barriers to implementing AI-assisted healthcare, and several examples have been developed in the US or Europe that address some of these.
„Many of these AI algorithms are trained in the US or Europe and most of the training data is from white or African Americans and people who don't share the same racial background. [as Thais]So they may not work as well in a Thai or Asian local environment as they do in a developed environment,” Trump said.
„For both practical and economic reasons, Thailand is very keen to develop their own AI industry and applications that can be used domestically,” he added. In part, some AI-driven healthcare systems developed abroad are more expensive to acquire and implement. Also, Thailand prefers solutions that fit the local context.
Some research work on AI for medical applications is ongoing within Thailand, with some companies hoping to release them to the market in the near future. “AI holds a lot of promise in healthcare. It's now being used based on chatbots, and it's being implemented for image recognition,” Trump said.
Present is very common. „But it needs to be very high-level data for health records for public health.”
Barriers identified through research
„The first task was to systematically map these barriers and enablers published by others, for example, in the academic literature outside of Thailand, that could affect data sharing, meaningful data collection, and quality. We then tested those barriers locally[in Thailand],” Tromp said.
In common with many countries in the region, he noted, „people use different software to collect data” in Thailand. Besides, “If you go to the lower levels of health care like primary care or if they use paper [patient] Records mean that you access data only from centers capable of collecting data”.
Fragmented healthcare delivery implies differences in data structure, standards and collection, and these hinder interoperability. In Singapore, TRUST, a data sharing platform run by Singapore's Ministry of Health, aims to improve health outcomes by collecting all of this data in one platform.
The platform includes research data ranging from genetics to socio-economic data and is sourced from public health institutions, research institutes and public agencies that allow access to their anonymized data for research purposes via TRUST.
However, Trump acknowledged that Singapore's example was expensive. Limited resources are a significant barrier, with uneven human, technical and financial resources across healthcare institutions. The high costs of hardware and software acquisition, installation and maintenance hinder quality data collection and sharing, particularly for smaller clinics and hospitals, the research found.
APRU's final report on 'AI for Social Good', to be released, „points to a lack of understanding of the value of data and the importance of data protection and privacy. Health literacy issues and confusion about data sharing parameters also contribute to the challenges. Additionally, precise data sharing regulations at political and policy levels and Lack of guidelines creates uncertainty and hinders progress.
Trump noted a reluctance to share data within the government but also outside the government, including hospitals and others that hold health data. Additionally, for many, Thailand's new Personal Data Protection Act, implemented in 2022, is unclear about how they can share data and in what formats. „This is one of our key findings. We recommend creating a protocol for this,” Trump said.
The project proposed a regulatory 'sandbox' to foster innovation within a protected testing environment with fewer regulatory constraints, so that relevant government departments can figure out what future regulation is appropriate.
„The rise of regulatory sandboxes in the health sector has been fueled by the exponential increase in digital health adoption in many countries,” the project noted. Trump said it was a suggestion of interest to the Thai government.
Working with policy makers
Research input is valuable and important in the fast-moving AI environment, Trump said. “AI has particular challenges for data sharing. Because of the granularity you demand from data to build AI, there are very few policy frameworks that directly address this and are therefore difficult to replicate. [from others]. You need new knowledge to inform policy improvements.”
International organizations such as the United Nations have on-the-ground knowledge but are rarely involved in knowledge creation, Trump pointed out. „Healthcare systems face many challenges, such as human resources, that require innovations such as AI to strengthen them, so universities have an important place to create knowledge.”
But it was important to work with the Thai authorities from the beginning. „With our Thai partners, we had several meetings before we came up with the final research questions, and we had a lot of people in those initial meetings,” explained ANU's Erskine.
The project also had peer reviewers who commented on the drafts prepared by the researchers. This includes Dr Greg Raymond, Assistant Professor at ANU
„I think the program did a good job of bridging the gap between research and policy,” Trump said. „Working with government to inform research priorities is very responsive – this is an unmet need in the region.”
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