Pharmaceutical Science: Research Trends and Challenges Vol. 2

Rural populations continue to experience reduced availability of clinicians, longer travel distances to diagnostic services and slower entry into specialist pathways than urban populations. Telemedicine has been promoted for three decades as a partial correction to this asymmetry, and artificial intelligence is now proposed as the component that will make remote care scalable by automating interpretation, triage and prioritisation. This critical narrative review examines whether the convergence of artificial intelligence and telemedicine has demonstrably improved access to health care in rural settings, and where the supporting evidence remains weakest. Literature was identified through open scholarly indexes, citation registries and institutional repositories, with searching completed on 3 July 2026, and was appraised for design adequacy, representativeness, outcome relevance and transferability rather than being catalogued descriptively. Four clinical domains dominate the evidence base: retinal screening, chest radiography for tuberculosis, obstetric ultrasound and asynchronous dermatology. Within these domains, diagnostic performance under controlled conditions is frequently strong and, for gestational age estimation and retinal grading, is comparable with or better than the conventional reference pathway. The evidence supporting improvements in access itself is substantially thinner. Most studies report accuracy rather than referral completion, time to treatment or population coverage, and prospective evaluations conducted under routine rural conditions have sometimes shown performance well below that reported in retrospective validation. Economic analyses are few, are concentrated in a small number of health systems, and generally model screening rather than the complete care pathway. Trust among rural service users is conditional and closely tied to continued clinician involvement. Persistent constraints include connectivity limitations that favour asynchronous and on-device designs, unrepresentative training data, fragmented regulation of cross-jurisdictional care, and the absence of downstream capacity to absorb the additional demand that effective case-finding generates. The field requires pragmatic trials with access outcomes, transparent external validation in rural cohorts, and economic evaluation of whole pathways rather than isolated diagnostic steps.