Abstract:
Cancer now ranks among the leading causes of death in Kenya, with approximately 35,867 new cases and 22,888 deaths annually, and outcomes remain constrained less by the availability of therapy than by the diagnostic pathway that must precede it. A national pathology workforce numbering in the low hundreds serves a population exceeding fifty million, and delayed or absent tissue diagnosis drives late presentation across the five malignancies that account for most of the national burden. Artificial intelligence offers a mechanism to extend interpretive capacity in a system where human expertise cannot be scaled at the rate the disease burden demands. This communication sets out a prioritized research agenda spanning smartphone-assisted visual inspection of the cervix, breast ultrasound classification and histopathological grading, morphological and immunophenotypic triage of hematolymphoid neoplasms, radiomic triage of thoracic and hepatic disease, and AI-assisted radiotherapy contouring. We present a six-stage methodological pipeline covering stakeholder engagement, local dataset curation, transfer learning, validation, usability assessment, and data governance, together with a tiered adoption framework matched to laboratory capability and the workforce competencies required for safe clinical supervision of these systems. The agenda is directed at early-career investigators and is transferable to comparable low- and middle-income settings.
