Title : Artificial Intelligence (AI) in public health: Innovations, related challenges, and possible solutions
Abstract:
The use of technology has been prevalent in healthcare and related domains. With the recent technological developments, Artificial Intelligence (AI) has been prevalent in the public health sector. Despite the widespread use of AI and its benefits in certain sectors, there have been multiple challenges in implementing AI across public health segments. This presentation throws light on the possible solutions (technological and strategic) to handle the challenges related to AI and obtain the maximum benefit of technology usage across public health sector. Technologically, AI encompasses machine learning, AI algorithms, and Generative AI (including large language models). The ability to execute these complex technologies has increased recently due to the infrastructure advancements supporting software development. For decades, technology has helped multiple areas of public health such as electronic health records, data analysis for public health interventions, communications and outreach, disease surveillance and warning, personalized health management etc. With the advent of AI and the infrastructure supporting advanced computation, the use cases for AI in public health have increased tremendously. Some AI-enabled technological use cases in public health include disease diagnosis, disease forecasting, public health surveillance, drug development, patient care, and claims processing. While the use cases are enormous, AI has significant challenges. Ethical and legal concerns, such as patient privacy and data security, pose substantial barriers. The potential for biased algorithms can lead to inequitable healthcare outcomes, raising questions about accountability and fairness. Additionally, data paucity—especially in underrepresented populations—can skew results and reduce the efficacy of AI models. Irregular data quality further complicates analysis, as inconsistent or incomplete datasets can lead to inaccurate predictions. Moreover, effective AI applications often rely on high-performance infrastructure, necessitating significant investment and technical expertise. Overcoming these hurdles requires a multi-disciplinary approach that addresses technical intricacies and prioritizes ethical considerations, ensuring that AI is a tool for equitable and effective public health solutions. The multi-disciplinary approach to prevention, protection and promotion of public health includes actions from multiple stakeholders including hospitals, governments as well as the public to ensure healthy environments.
