Cognitive AI-Assisted Primary Care Health Delivery: A Pilot Study in Bangladesh

Research has documented persistent physician workforce shortages globally, with projected shortfalls threatening primary care access in underserved populations. Existing AI applications in healthcare have largely focused on predictive risk-scoring tools that generate probability estimates but do not reduce the time a physician spends completing a patient encounter. A January 2025 study further demonstrated that large language models lack the metacognitive capacity necessary for reliable medical reason ing, i.e., being able to ask appropriate questions in the absence of information to collect patient history and update differential diagnoses. This paper reports on a 2025 pilot deployment of ClinicalAssist in Bangladesh that tested a fundamentally different model: An AI system designed to replicate every step of the clinical workflow. Across 239 unique patients, 277 encounters, and 287 diagnostic opportunities, the system achieved an overall diagnostic accuracy of 94.7%, with chronic disease accuracy of 98.0% and acute care accuracy of 88.9%. These results suggest that cognitive AI has the potential to be a powerful clinical force multiplier if properly integrated in workflow.