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 |
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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 |
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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 Leads | |
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Ying Su |
, Pfizer |
Radha Railkar |
, Merck |
radha_railkar@merck.com |
Nicola Newton, PHUSE Project |
Assistant |
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Evidera
Munish Mehra
Tiger Medical Group
Philip He
Data Sciences International
Unnat Patel
AnalysisMate
Shakti Prasanna Tirni
Docs Global
Ying Su
Pfizer
Stuart Malcolm
Veramed
Kausar Riaz Ahmed
Pfizer
Mike Hamdi
Pfizer
Nimita Limaye
International Data Corporation
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Objectives & Deliverables | Timelines |
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Identify the industry knowledge-sharing community of practice, prioritise future project topics | Q2/3 2023 |
PHUSE/FDA CSS presentation/poster | Q3 2023 |
Start gathering use cases | Q4 2023 |
Quarterly Community Forums |
Invited expert talks | Q1 |
Status | ||||
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2024 |
Q2 2025 |
AI/ML Sub-teams |
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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 |