Achieving health equity in immune disease: leveraging big data and artificial intelligence in an evolving health system landscape

Asif H Khan^1*Stan Kachnowski^2,3Shadé Floquet⁴Kendal Whitlock⁵Juan Pablo Wisnivesky⁶Daniel B Neill⁷Irene Dankwa-Mullan⁸Gezzer Ortega⁹Moataz Daoud¹⁰Raza Zaheer¹¹Maia Hightower¹²Paul Rowe¹³

• ¹Global Medical, Immunology, Sanofi, Bridgewater, NJ, United States
• ²Healthcare Innovation and Technology Lab, New York, NY, United States
• ³Digital Health Program, Columbia Business School, Columbia University, New York, NY, United States
• ⁴Corporate Affairs, Speciality Care, Sanofi, Paris, France
• ⁵RWE Clinical Trials, Walgreens Boots Alliance, New York, NY, United States
• ⁶Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
• ⁷Machine Learning for Good Laboratory, Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, United States
• ⁸Department of Health Policy and Management, Milken Institute School of Public Health, George Washington University, Washington, DC, United States
• ⁹Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
• ¹⁰Dermatology and Rheumatology, Sanofi, Bridgewater, NJ, United States
• ¹¹Immunology, Sanofi, Bridgewater, NJ, United States
• ¹²Equity AI, Park City, UT, United States
• ¹³Sanofi Specialty Care, Sanofi, Bridgewater, NJ, United States

Prevalence of immune diseases is rising, imposing burdens on patients, healthcare providers, and society. Addressing the future impact of immune diseases requires comprehensive 'big data' on global distribution/prevalence, patient demographics, risk factors, biomarkers, and prognosis to inform prevention, diagnosis, and treatment strategies. Big data offer promise by integrating diverse real-world data sources with artificial intelligence (AI) and big data analytics (BDA), yet cautious implementation is vital due to the potential to perpetuate and exacerbate biases. In this review, we outline some of the key challenges associated with achieving health equity through the use of big data, AI, and BDA in immune diseases and present potential solutions. For example, political/institutional will and stakeholder engagement are essential, requiring evidence of return on investment, a clear definition of success (including key metrics), and improved communication of unmet needs, disparities in treatments and outcomes, and the benefits of AI and BDA in achieving health equity. Broad representation and engagement are required to foster trust and inclusivity, involving patients and community organizations in study design, data collection, and decision-making processes. Enhancing technical capabilities and accountability with AI and BDA are also crucial to address data quality and diversity issues, ensuring datasets are of sufficient quality and representative of minoritized populations. Lastly, mitigating biases in AI and BDA is imperative, necessitating robust and iterative fairness assessments, continuous evaluation, and strong governance. Collaborative efforts to overcome these challenges are needed to leverage AI and BDA effectively, including an infrastructure for sharing harmonized big data, to advance health equity in immune diseases through transparent, fair, and impactful data-driven solutions.