Opportunities and challenges of healthcare digital entrepreneurship focused on AI systems in enhancing health equity: Final report

Advances in technological development are ever-increasing at all times; consequently, a rapid increase and changes in digital technology have revolutionized healthcare delivery globally (Mohammed-Nasir et al., 2023). Digital health through artificial Intelligence, telemedicine, genomics, robotics and many other innovative technologies, is already changing in many ways how medicine is being practiced around the world. They have simplified access to health, lower cost of diagnosis and treatments, and improved communication between doctors and patients in the areas of electronic health (eHealth), storage of and access to medical information and data, generating and storing of big data, improving lines of communication between patients and their doctors, electronic health records (EHRs), telemedicine and telehealth, mobile health (mHealth), online learning (eLearning), health applications, and drones. (Mohammed-Nasir et al., 2023) Given the transformative impact of digital technologies on healthcare, particularly the rise of AI systems, our guiding question is what opportunities and challenges do healthcare digital entrepreneurs encounter in their efforts to enhance health equity? Digital Health Entrepreneurship Digital health entrepreneurship is the pursuit of opportunity within healthcare characterized by scarce and uncontrolled resources, with the goal of creating user-defined value through the design, development, roll or launch, and harvesting of digital health innovative products, services, platforms, and models (Zajicek & Meyers, 2018). Healthcare, encompassing the prevention, treatment, and management of medical conditions, aims to enhance mental and physical well-being. Health equity, as defined by the US Department of Health and Human Services, aims to eradicate avoidable health disparities among socioeconomic, demographic, and geographic groups, particularly concerning access to diagnosis and treatment across various health conditions. With the rapid advancement of digital health technologies and AI/ML-enabled medical devices in recent years, the integration of AI into healthcare presents both promise and challenges. AI, whether used alone or in conjunction with healthcare providers, presents opportunities to improve outcomes for patients and populations. Ethical frameworks are vital for assessing the potential positive and negative implications of extensive data collection, analysis, and utilization in healthcare processes. Bioethical principles like beneficence, autonomy, and equity, are sought to determine a process's ethical balance. Illustratively, sensitivity, specificity, and clinical 2 outcomes can measure beneficence, while metrics for equity may include sensitivity disaggregated by demographic subgroup or population-achieved sensitivity (Abràmoff et al., 2023). These considerations focus on determining how well a healthcare process, potentially involving AI, aligns with the equity principle. Despite improvements in average healthcare outcomes, the integration of AI may unevenly distribute benefits across demographic groups, leading to substantially worse outcomes for some.