PAVE

PAVE addresses a central dilemma in cervical-cancer prevention: HPV testing can identify women at higher risk, but programmes still need a practical way to decide who requires closer evaluation or treatment. If triage is too insensitive, important disease may be missed; if it is too broad, limited clinical and treatment capacity can be overwhelmed.

The multinational consortium studies how HPV testing and extended genotyping can be combined with cervical imaging and automated visual evaluation to support more targeted screen-triage-treat pathways. Tanzania participates within this wider scientific collaboration. The programme brings molecular risk information and image-based assessment together rather than treating them as separate innovations.

PAVE is also an implementation story about the conditions under which artificial intelligence can be used safely. An algorithm cannot interpret an unusable image, and a technically accurate output has limited value if clinicians do not know how to act on it or if treatment pathways cannot respond. The project therefore studies image quality, model performance, population variation and clinical integration alongside the core diagnostic question. Its goal is not to replace provider judgement, but to understand whether combined risk signals can help screening programmes target care more precisely across diverse settings.

The scientific question is therefore not whether AI is inherently better, but whether molecular and image-based information can be combined safely enough to improve triage decisions in real screening programmes.