BELFAST, UK, September 23, 2026 – Global Health Connector and Qualcomm Technologies, Inc. announced the launch of the Personal AI Health Alliance, a global community of healthcare and technology leaders with a mission to drive the use of personal AI solutions to help transform healthcare provisions worldwide.
The Alliance aims to accelerate the commercialization of AI-enabled wearable health solutions that leverage on-device intelligence to enable evidence-based outcomes and help scale personalized care. By bringing together expertise from across the healthcare and technology ecosystem, members will explore how intelligent wearables can make health intelligence more continuous, personalized, and accessible.
The Alliance is centered around three key beliefs:
The Personal AI Health Alliance early members include Optum, Scripps Health, MERCK, Digital Medicine Society, Biocom, and others. The complete list of members can be found at the Personal AI Health Alliance website located at PAIHealth.org
“Healthcare is data-rich but often insight-poor. The real opportunity lies in turning data into actionable intelligence that improves outcomes for everyone. PAIHA has the potential to enable true ‘CollaborAction’ across providers, payors, technology companies, medTech and pharmaceutical organizations to unlock the full value of health data. This has the potential to deliver intelligent health for everyone, improve wellbeing, add healthier years to life, and transform healthcare from an illness system to a wellness system. That is why we are delighted to be founding members of PAIHA alongside Qualcomm and to use our global network of 90 Ecosystems and multistakeholder communities to promote the activities of PAIHA.” Bleddyn Rees, Global Health Connector
“The Personal AI Health Alliance is a new global alliance uniquely focused on bringing Personal AI and intelligent wearables into healthcare. We believe this is an important inflection point, as we aim to bring the best of healthcare and technology together for more accessible, personalized and proactive healthcare experiences,” said Ziad Asghar, Senior Vice President and General Manager of XR, Wearables and Personal AI, Qualcomm Technologies, Inc.
“At Merck KGaA, Darmstadt, Germany, we see an important opportunity to move from episodic care toward more continuous, proactive, and personalized health support. We believe the best healthcare combines High Touch and High Tech. Meaningful health insights can emerge between appointments, not only during them, and new digital measures, intelligent devices, and responsibly designed AI can help us better understand those experiences. Through the Personal AI Health Alliance Program, we want to translate those insights into richer evidence and ultimately more integrated, patient-centered healthcare," said Emre Ozcan, Global Head of Integrated Health & Medicines, MERCK
"Some of the most meaningful advances in healthcare happen when organizations come together across disciplines and industries. PAIHA creates an important opportunity to explore how connected technologies can help expand access to health insights and support more personalized health experiences," noted Dr. Neil Parikh, Chief Medical Officer, Value-Based Care, Optum Health
"Healthcare professionals are increasingly looking for ways to better understand what happens between traditional points of care. PAIHA brings together healthcare and technology leaders to explore how AI and intelligent wearables can help provide richer context and more meaningful insights that support patient care," commented Anil N. Keswani, MD, Corporate EVP & Chief Medical and Ops Officer, Ambulatory Care, Scripps Health
Latest updates from Personal AI Health Alliance members, including proofs of concept, case studies, and upcoming events will be made available at the Personal AI Health Alliance website.
For more information, please visit the Personal AI Health Alliance website at PAIHealth.org
1 Based on
https://www.pharmafocuseurope.com/clinical-trials/benefits-challenges-using-wearables and https://pmc.ncbi.nlm.nih.gov/articles/PMC11461032/