Editorial: Navigating the Nexus of Big Data, AI, and Public Health: Transformations, Triumphs, and Trials in Multiple Sclerosis Care Access The Convergence Revolution in Health and Society

Prof. Immanuel Azaad Moonesar^1,2*Dr. Manoj Kumar M V³Khulood Alsayegh^1,4Ayat Abu-Agla⁵Likewin Thomas³

• ¹Mohammed Bin Rashid School of Government, Dubai, United Arab Emirates
• ²International Vaccine Institute, Gwanak-gu, Republic of Korea
• ³NITTE Meenakshi Institute of Technology, Bengaluru, India
• ⁴Dubai Health Authority, Dubai, United Arab Emirates
• ⁵University of Birmingham Dubai, Dubai, United Arab Emirates

[Joshi] provides a critical examination of how big data and AI technologies can both advance and hinder gender equality in healthcare access and treatment. This perspective article addresses one of the most pressing ethical challenges in contemporary healthcare technology: bias is a big challenge when implementing AI systems in medical settings, particularly relevant for conditions like Multiple Sclerosis, where gender disparities in diagnosis and treatment access are well-documented.The contribution by [Joshi] highlights three critical categories where machine learning can facilitate accessible, affordable, personalized, and evidence-based healthcare for women, while simultaneously addressing the algorithmic bias that threatens to undermine these advances. This work exemplifies the critical need for intersectional approaches to healthcare AI development that consider both the transformative potential and the ethical implications of these technologies in specialized treatment contexts such as neurological care. [Chen et al.] contribute valuable insights into the practical challenges of leveraging social media data for public health research and healthcare accessibility analysis. Social media has profoundly changed our modes of self-expression, communication, and participation in public discourse, generating volumes of conversations and content that cover every aspect of our social lives. Their research addresses the critical gap between the theoretical potential of social media analytics and the practical hurdles researchers face in accessing and utilizing these data sources for understanding patient experiences and healthcare navigation challenges.The work by [Chen et al.] is particularly relevant for understanding patient perspectives on healthcare accessibility, treatment barriers, and health-seeking behaviors-essential components for developing comprehensive models of healthcare access, such as those needed for MS treatment planning as an example of the United Arab Emirates. Social media platforms provide unprecedented access to real-time patient sentiment, treatment experiences, and geographic variation in healthcare access that can inform policy and practice improvements. The fourth contribution explores advanced techniques in knowledge-based recommendation systems, with direct relevance to clinical decision support and personalized treatment recommendations for complex conditions like Multiple Sclerosis. This research demonstrates how sophisticated recommendation algorithms can be applied to support clinical decisionmaking in specialized care contexts, patient education about treatment options, and healthcare resource allocation-all critical components for ensuring equitable access to Disease-Modifying Therapies.The integration of knowledge-based systems with machine learning approaches represents a significant advancement in creating transparent, including clinically interpretable, explainable AI tools for specialized healthcare settings. This work addresses the crucial challenge of AI interpretability in neurological care contexts, where understanding the rationale behind treatment recommendations is essential for both clinical acceptance and patient adherence to complex therapeutic regimens. When handling private health information, strong protections are needed to prevent breaches and unauthorized use. Across all four articles, several critical themes emerge that are directly applicable to addressing geographic and socioeconomic disparities in specialized healthcare access, particularly relevant for conditions requiring ongoing Disease-Modifying Therapy. The methodological approaches demonstrated across these studies provide frameworks for mapping healthcare accessibility patterns.[Santos et al.]'s geographic analysis of educational access barriers during the pandemic offers direct parallels to understanding how distance, infrastructure, and socioeconomic factors create barriers to specialized medical care in diverse geographic regions, including accessibility heatmaps or location allocation models, which are used in health system planning to reduce spatial inequality Algorithmic Bias and Treatment Equity: [Joshi]'s examination of gender bias in healthcare AI systems highlights challenges that extend to all aspects of specialized care delivery. The research demonstrates how historical inequities in healthcare access can be embedded in algorithmic systems used for treatment allocation, facility planning, diverse training data and patient risk stratification-directly relevant to ensuring equitable DMT access across different populations. The integration of quantitative analytics with qualitative patient experience data, demonstrated by [Santos et al.] and supported by [Chen et al.]'s social media analysis framework, provides essential methodological guidance for comprehensive healthcare accessibility studies that combine geospatial analysis with patient journey mapping. The research presented in this collection moves beyond theoretical accessibility models to address practical implementation challenges in healthcare delivery. [Chen et al.]'s analysis of data collection hurdles provides essential guidance for researchers conducting patient experience studies, while [Santos et al.]'s socioeconomic analysis offers actionable insights for policymakers addressing geographic healthcare disparities.These studies demonstrate that successful implementation of equitable healthcare access requires not only advanced analytical capabilities but also careful attention to privacy protection, stakeholder engagement, and systematic approaches to addressing the social determinants that influence treatment access and adherence patterns. Policymakers can use Big Data to subsequently review the social factors, among others, behind these health disparities. The collective insights from these four articles point toward several critical areas for future research in healthcare accessibility, with direct applications to specialized treatment access challenges.The development of comprehensive accessibility indices that integrate geographic, socioeconomic, and system-level factors becomes increasingly crucial for conditions requiring complex, ongoing treatment regimens. The methodological frameworks demonstrated in this collection provide essential building blocks for creating predictive models that can identify patients at risk of treatment discontinuation and guide targeted intervention strategies.Furthermore, the research highlights the importance of real-time monitoring systems that can track accessibility patterns and identify emerging barriers to care. The integration of social media analytics, demonstrated by [Chen et al.], with geospatial analysis approaches offers promising directions for developing systems that monitor healthcare accessibility in real time.