PARTICIPATORY medicine could help transform hepatology by widening public involvement in research, prevention and education, according to a new conceptual study.
The approach centres on collaboration between clinicians, researchers, laypeople and AI to strengthen trust and engagement while generating insights beyond traditional clinical settings.
Liver disease, which includes conditions such as alcohol-associated liver disease, metabolic dysfunction-associated steatotic liver disease and viral hepatitis, remains a growing health burden despite being largely preventable.
Rising Liver Disease Burden In the UK
More than nine in ten liver disease cases are considered preventable, yet rates continue to climb.
In the UK, premature liver-related deaths have increased by 93% over two decades. Hospital admissions have doubled in ten years, and liver disease remains the only major cause of death with steadily rising rates since the 1970s, a trend that may partly reflect changes in death certification practices.
Liver-related health information in the UK is fragmented across NHS platforms, unlike more coordinated national campaigns seen in cardiovascular disease, diabetes and cancer.
For these reasons, broader societal involvement is increasingly viewed as a way to address gaps in awareness and early risk identification.
Borrowing from Citizen Science Models
The study draws on established citizen science models, defined as scientific work undertaken by the public in collaboration with professional researchers. Such approaches have been used in fields such as astronomy, molecular biology and meteorology, and shows how large-scale participation and AI can create co-learning systems.
The study outlined six hepatology-focused concepts. Public-led initiatives include Liver Zoo for imaging annotation, Liver Cache and LiverQuest for prevention and behavioural awareness, and Heporama for community-based toxin surveillance. Patient-centred projects include a Liver Cancer Wisdom Bank and Liver4Mind, which targets neurocognitive monitoring in cirrhosis.
Addressing Stigma and AI Challenges
Stigma linked to liver disease, often tied to alcohol use, obesity or drug use can deter involvement. AI also introduces complexity, influencing how data are collected and interpreted.
Participation often focuses on diagnosed patients, potentially missing at-risk populations.
Caution Needed in Implementation
The proposals remain conceptual and are not yet tested in practice. Challenges include sustaining engagement, ensuring diverse participation and maintaining data quality.
Volunteer-generated data may introduce bias, which could limit generalisability and affect predictive models.
Implications for Hepatology Practice
Despite limitations, the framework outlines potential ways to expand prevention efforts and strengthen public trust.
By integrating community input with clinical expertise and digital tools, participatory medicine could support earlier intervention and more representative data collection.
For hepatologists and policymakers, the findings suggest new, scalable approaches to engage populations beyond traditional healthcare settings, particularly those at risk but not yet in care.
Reference
Laevens BPM et al. The future of hepatology: preventing liver disease together with the public, scientists and artificial intelligence. JHEP Reports. 2026;DOI: 10.1016/j.jhepr.2026.101846.
Featured image: Maria Vitkovska on Adobe stock
