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    AI/ML in Digital Health Technologies (DHTs)

      AI/ML in Digital Health Technologies (DHTs)

      Jun 26, 2025
        

      Project Scope 

      The use cases of artificial intelligence and machine learning (AI/ML) in digital health technologies to improve healthcare through software. Understand the challenges, and identify the gaps. Connect different stakeholders, share knowledge, and advance in developing AI/ML in DHTs.  

      Project Statement 

      DHTs are revolutionising the healthcare industry, with AI/ML playing a key role in the development of new solutions. With more applications of AI/ML in practice, from optimising workflows to improving diagnostic capabilities, the collaborations to learn from the use cases and the partnership to overcome challenges are urgently needed.

      Project Impact 

      The integrated effort to study real-world applications will ensure the emerging technologies are used effectively and in compliance with relevant guidelines and regulations.

      Project LeadsEmail

      Ying Su, Pfizer

      ying.su2@pfizer.com

      Radha Railkar, Merck

      		radha_railkar@merck.com
      Nicola Newton, PHUSE Project Assistant

      nicky@phuse.global

      CURRENT STATUS Q2 2025

      • Project is closed.​​

      Objectives & Deliverables

      Timelines

      Identify the industry knowledge-sharing community of practice, prioritise future project topicsQ2/3 2023
      PHUSE/FDA CSS presentation/posterQ3 2023
      Start gathering use cases Q4 2023

      Quarterly Community Forums

      Invited expert talks 

      		Q1 2024
      Q2 2025

      Sub-Team

      Forum Topic

      Lead

      GA

      Generative AI in healthcare

      Jeffrey Lavenberg

      AP

      Application of AI/ML in precision medicine (includes RWE)

      Shraddha Thakkar

      RL

      Regulatory landscape of AI/ML in DHTs (current landscape, knowledge gaps, best practices for regulatory submissions, challenges of regulating AI)

      Richard Baumgartner

      MD

      AI/ML models (logistic regression, support vector machines, decision tree, convolutional neural networks, etc.)

      Hanming Tu

      UC

      Challenges of use of AI/ML in DHTs (ethical concerns, privacy issues/cybersecurity, misuse of data, complexity of data management including data interoperability, etc.)

      Jessica Hu

      SD

      Software-driven medical devices

      Anders Vidstrup

      , multiple selections available,
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