From Development to Deployment of AI-assisted Telehealth and Screening for Vision- and Hearing-threatening diseases in resource-constrained settings: Field Observations, Challenges and Way Forward

2025-09-18

AI Summary

In resource-limited settings, early screening for ophthalmic and otologic diseases is hindered by critical shortages of specialists, inadequate diagnostic equipment, and the difficulty of transitioning paper-based workflows to AI-ready digital systems. Method: This study proposes an end-to-end, iterative co-design methodology that tightly integrates AI model development—including transfer learning and automated image quality assessment—with digital health workflow reengineering. Field-based prototyping, shadow deployment, and continuous feedback loops were employed to rigorously evaluate system usability and operational feasibility. Contribution/Results: We introduce a novel “AI–Workflow–Human Factors” triadic framework for localized adaptation, distilling reusable deployment insights and evidence-based strategies for overcoming key implementation barriers—such as workflow misalignment, clinician trust deficits, and infrastructure constraints. The resulting dual-dimensional (technical and managerial) guidance framework enables sustainable, scalable deployment of AI-assisted screening programs in low-resource environments.

Technology Category

• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Vision- and hearing-threatening diseases cause preventable disability, especially in resource-constrained settings(RCS) with few specialists and limited screening setup. Large scale AI-assisted screening and telehealth has potential to expand early detection, but practical deployment is challenging in paper-based workflows and limited documented field experience exist to build upon. We provide insights on challenges and ways forward in development to adoption of scalable AI-assisted Telehealth and screening in such settings. Specifically, we find that iterative, interdisciplinary collaboration through early prototyping, shadow deployment and continuous feedback is important to build shared understanding as well as reduce usability hurdles when transitioning from paper-based to AI-ready workflows. We find public datasets and AI models highly useful despite poor performance due to domain shift. In addition, we find the need for automated AI-based image quality check to capture gradable images for robust screening in high-volume camps. Our field learning stress the importance of treating AI development and workflow digitization as an end-to-end, iterative co-design process. By documenting these practical challenges and lessons learned, we aim to address the gap in contextual, actionable field knowledge for building real-world AI-assisted telehealth and mass-screening programs in RCS.

Problem

• Developing AI-assisted telehealth for vision and hearing diseases
• Addressing deployment challenges in resource-constrained settings
• Implementing scalable screening with AI-ready workflows

Innovation

• Iterative interdisciplinary collaboration for co-design
• Public datasets and AI models despite domain shift
• Automated AI-based image quality checks