AI models looked accurate for early dengue diagnosis—but the evidence rarely left the lab

This is mainly dengue evidence from Asian hospitals

Forty-one of the 43 studies assessed dengue. Thirty-five involved Asian settings, including 12 studies from India, while evidence for Zika and chikungunya was sparse. The search found no eligible model study for West Nile virus, yellow fever or Rift Valley fever. Results should therefore not be stretched across pathogens whose symptoms, prevalence, laboratory pathways and patient populations differ.

Twenty studies used retrospective diagnostic-accuracy designs and ten modelled existing datasets. Only five were clearly prospective: three diagnostic-accuracy studies, one cohort and one modelling study. Most drew on clinical and laboratory variables available around assessment, and 37 used laboratory confirmation in at least part of the reference standard. Yet only a few studies used genuinely separate temporal or cross-dataset validation.[1]

Bias, calibration and workflow evidence are the missing pieces

Using the PROBAST prediction-model tool, the reviewers rated 30 studies—69.8%—at high overall risk of bias, seven at low risk and six unclear. Missing-data and class-imbalance handling were often poorly reported. Only four studies presented a formal or semi-formal calibration check. Calibration asks whether a stated risk behaves like that risk in practice; good discrimination alone cannot tell a clinician whether a threshold will produce an acceptable number of false alarms or misses.

Forty studies remained research prototypes and three were pilots or proofs of concept; none reported routine deployment. The review also found no consistent evidence that one model family outperformed another. A useful next study would freeze a model and threshold, test it prospectively across several countries, report calibration and subgroup performance, and compare ordinary care with an AI-supported pathway. It should count delayed or missed diagnoses, unnecessary testing, clinician workload and patient outcomes—not only retrospective accuracy.

The review was registered prospectively, used duplicate screening and extraction, and its authors reported no specific funding or competing interests. Its own limits include English-language, peer-reviewed publications and the possibility of publication bias. The careful conclusion is not that AI has failed, but that early diagnostic promise has outrun the evidence needed for dependable use across outbreaks and health systems.[1]