AI/ML in Digital Health Technologies (DHTs)

  

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 Q3 2024

  • 3rd quarter Community Forum on topic of regulatory landscape scheduled for 25 September.

  • Sub -teams continue to meet and plan Community Forums.

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


AI/ML Sub-teams

The project volunteers are organised in sub-teams to learn a specific topic through planning/facilitating a forum with experts, and collecting use cases.  Please indicate your participation by filling out the form to join the sub-team of your interest

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