Jul 2026· Information Hiding· pp. 1-7· 0 citations· 41 references
Computer Science
TL;DR
Overall, the paper synthesizes design principles for human-centered AI that emphasize localization, explainability, training, and accountability, arguing that effectiveness depends more on interaction design than technical sophistication.
Abstract
Low-resource healthcare systems in South Asia and Africa face severe constraints in infrastructure, connectivity, data availability, and digital literacy that shape how artificial intelligence can be deployed. This paper examines human–AI interaction in these settings, focusing on patients, community health workers, and clinicians rather than model performance alone. Drawing on empirical studies and case examples from Bangladesh, India, and Nigeria, it shows that AI systems designed for high-income contexts often fail when transferred without adaptation. Key challenges include language diversity, absence of electronic health records, limited AI literacy, trust deficits, and unresolved ethical and liability concerns. The analysis demonstrates that localized, multilingual, human-in-the-loop AI can meaningfully augment care when integrated into existing workflows and mediated by trusted health workers. Overall, the paper synthesizes design principles for human-centered AI that emphasize localization, explainability, training, and accountability, arguing that effectiveness depends more on interaction design than technical sophistication.
Global health inequities continue to restrict access to timely and quality care in low- income settings Which have limited infrastructure, workforce shortages, financial barriers & geographic disparities that remain persistent challenges. Emerging advancements in artificial intelligence, particularly agentic AI, offer new opportunities to strengthen healthcare delivery in the resource constrained environments. These systems can support in decision making, improve early diagnosis, and enhance service reach.
This chapter adopts a narrative literature review and conceptual analysis. It synthesises the evidence from peer reviewed studies, global health reports, and the policy documents. The analysis examines applications of agentic AI in areas such as telehealth, disease surveillance, clinical support and the community based care. It also evaluates critical concerns including digital exclusion, algorithmic bias ,data privacy, and infrastructural limitations.
The chapter argues that agentic the AI can help in reducing healthcare disparities when implemented within inclusive and ethically governed frameworks. It also highlights the need for context-sensitive design, local capacity development & the supportive policy environments. The study also provides the structured understanding of how AI-driven solutions can help in improving accessibility, efficiency and equity in the underserved populations.
Dr. Sanmeet Kour Bali, Prithviraj Banerjee· International journal of res...· 0 citations
Artificial Intelligence (AI) has become an emerging field with the potential to revolutionize the healthcare sector. Despite healthcare digitization, the problem of healthcare access inequalities still exists. For instance, in developing countries, there is often an insufficient number of skilled professionals and healthcare facilities, resulting in inadequate diagnosis, treatment, and healthcare access. Thus, the current paper examines the role of artificial intelligence in healthcare equality of the underserved population in Asia and Africa, based on a scoping review of AI-led healthcare studies and reports. The review aims to explore the possibilities of AI in transforming healthcare for the better in low-and middle-income countries and focus on how these solutions can be implemented in the context of social justice and health equity. The review follows the PRISMA-ScR guideline and includes the application of AI in diagnosis, telehealth, disease prediction and surveillance, patient monitoring, and healthcare management. The results of the research indicate that the implementation of AI in healthcare can make it more accessible and equitable by ensuring the early detection of diseases, providing access to remote healthcare, and optimizing treatment and management in both urban and low-resource settings. Nevertheless, there are several barriers to the adoption of AI in healthcare, including the lack of digital infrastructure and healthcare data and the ethical, regulatory, and technical challenges associated with the technology. Overall, the research identifies the key aspects of AI application in health care and emphasizes their importance in promoting healthcare equity in developing countries. The research contributes to the existing literature by providing a conceptual basis for addressing health disparities in low and middle-income countries (LMICs) through the lens of digital technologies. The study recommends that future interventions prioritize equitable, affordable, sustainable, and ethical healthcare through the implementation of AI technologies, algorithms, and programs.
Precision Medicine (PM) promises to tailor prevention, diagnosis, and treatment to individuals using molecular, clinical, and lifestyle data. For example, advances in genomic sequencing, large-scale biomedical datasets, and machine learning are accelerating this vision, particularly in the fields of oncology and pharmacogenomics. Yet, beyond technical developments, PM introduces a range of socio-technical challenges when implemented in real-world healthcare settings. These include interpreting probabilistic predictions in clinical practice, communicating uncertainty to patients and clinicians, integrating data-intensive tools into complex healthcare workflows, and addressing concerns around privacy, cost, and equitable access. Such challenges suggest that PM cannot be understood purely as a biomedical or computational problem, but also requires attention to interaction, interpretation, and governance. This Birds of a Feather (BoF) session brings together researchers and practitioners across HCI, Interactive Health (IH), clinical medicine, and design to discuss how HCI perspectives can help make PM interpretable, workable, and equitable in practice.
Yuhao Sun, Shuhao Ma, W. McLoughlin et al.· Information Hiding· 2 citations
Trust remains a fundamental pillar of the physician–patient relationship, ensuring care that is safe, evidence-based, and grounded in shared decision-making; however, the increasing use of artificial intelligence tools such as ChatGPT has introduced a new dynamic in how patients interpret symptoms and seek medical advice. This narrative describes a clinical encounter involving an elderly patient with multiple myeloma whose caregiver used ChatGPT to evaluate new-onset slurred speech suggestive of stroke, resulting in an urgent recommendation driven by incomplete clinical information. Through comprehensive history-taking, physical examination, and careful review of the Artificial Intelligence (AI)-generated response, the physician reassured the caregiver, contextualized the limitations of AI input, and provided guidance on its appropriate use. Rather than undermining trust, this interaction demonstrated that AI can serve as a catalyst for patient engagement and timely care-seeking, while offering physicians an opportunity to validate concerns, enhance communication, and reinforce clinical reasoning. When integrated thoughtfully into clinical conversations, AI has the potential to strengthen trust, improve patient satisfaction, and support safer, more collaborative care.
Saria Gouher· Patient Experience Journal· 0 citations