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Translating and Scaling AI Technology for Healthcare

Industry workshop provides AI in Health Grand Challenge teams with a platform to share their progress and future plans, and to reach out to potential partners

What’s next for the teams of the AI in Health Grand Challenge? This was the focus of an industry e-workshop on “Translating and Scaling AI Technology for Healthcare” which was held on 19 November 2020.

The AI in Health Grand Challenge is a $35m programme to fund AI research to support primary care teams in reducing complications arising from hyperlipidaemia, hyperglycaemia and hypertension (3H).

In the two years since its launch, the three awarded teams have made substantial progress towards the goal of using AI to help slow disease progression in 3H patients. One of them has developed an AI-assisted 3H Care (A3C) system to identify pre-3H persons, assess the status of 3H patients, and provide personal or group-based interventions through gamification. Another is rolling out an AI platform, JARVIS-DHL, which can be used to improve the care delivery process by facilitating evidence-based personalised care and shared-decision making. The third is aiming to catalyse mass adoption of AI in healthcare with Explainable AI as a Service.

The workshop provided a platform for the three teams to share their progress and future plans, and brought researchers and clinicians together with industry experts to discuss the translation and deployment of AI research and technology into scalable healthcare solutions.

In her welcome remarks, Prof Leong Tze Yun, Director of AI Technology, AI Singapore, highlighted the special features of the AI in Health Grand Challenge and its focus on measurable objectives and translation pathways. She also spoke about the technical challenges of effective translation and reiterated AI Singapore’s commitment to continue supporting the teams in their journey by helping them to source for additional data and funding opportunities.

Discussing AI Advances for 3H care

Representatives from the three teams took part in a panel discussion on “AI Advances for 3H Care” which was moderated by Dr Stefan Winkler from AI Singapore. A3C was represented by Dr Wang Di from the School of Computer Science and Engineering at Nanyang Technological University (NTU); JARVIS-DHL by Prof Ng See Kiong from the Computer Science Department, National University of Singapore (NUS); and Explainable AI as a Service by Prof Dean Ho from the Biomedical Engineering Department of NUS. Joining them was Mr Sutowo Wong, Director of Analytics & Information Management at the Ministry of Health (MOH), who shared MOH’s perspective on the use of AI for treating and managing chronic diseases.

The panellists discussed challenges related to medical data such as variability between patients or missing information, the issues involved in AI-based medical decision making; the explainability of machine learning models and the role of users (both patients and medical practitioners) in the process.

Delving into Integration Challenges

Another topic that was discussed at the industry workshop was the “Integration and Deployment of Healthcare Solutions”. This second panel session was moderated by Mr Chua Chee Yong from Integrated Health Information Systems. The speakers delved into the challenges of integrating healthcare data across public and private healthcare institutions, and the role of the National Electronic Health Records in consolidating, streamlining and standardising patients’ records to make the information shareable with health care providers.

They also highlighted the main hurdles for the deployment of new health technology solutions and how they may be overcome, and discussed the key technological, operational and design considerations that could help drive the adoption of AI healthcare solutions in Singapore.

The panellists included Prof Robert Morris from the MOH Office for Healthcare Transformation, Dr Sue-Anne Toh from Novi Health, Mr Gavin Teo from Altara Ventures, and Dr Frank Qiu from User Experience Researchers.

Progress and Future Plans
The workshop also created opportunities for the teams to link up with potential partners to do this. Breakout sessions were organised to allow representatives of the Grand Challenge teams to network with workshop participants and discuss more details about their work, the use cases of their research, and potential collaboration.

The virtual event attracted more than 170 participants, about half of whom were from hospitals and private sector organisations. Significantly, more than two thirds of the registrants were senior professionals including C-level executives, directors and professors.

As we move into the second stage of the AI in Health Grand Challenge next year, it will be important for the teams to collaborate with the right partners for deployment. They have made significant progress so far, demonstrating compelling prototype solutions for a wide range of applications, and we are heartened to see the high level of industry interest in their work.



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