Abstract
Abstract
Recent advances in sensor technologies and machine learning have increased expectations for actionable disease detection in dairy cattle, yet farm-level adoption remains limited. This conceptual review outlines an implementation trajectory from proof of concept to deployment as follows: (i) case definitions and defensible reference standards for diseases such as mastitis, lameness and respiratory disease should be biologically based; (ii) model development should consider features that drive utility and address class imbalance, ensuring that algorithms can reliably detect diseases that occur infrequently within herds; (iii) external validation should occur at the herd level, across seasons and time, and should report prevalence-aware metrics (e.g., recall and precision [positive predictive value]) rather than relying solely on accuracy or receiver operating characteristic area under the curve; and (iv) interoperability and economic viability should be sufficient to support multi-sensor use and sustainable adoption. Economic factors such as hardware and software costs, follow-up time burden, usefulness and return on investment under realistic false-positive rates will primarily determine adoption. Ethical and welfare considerations include maintaining human oversight where possible, avoiding invasive sensing and preventing over-automation. In conclusion, sensor-based systems show promise but remain constrained by inconsistent case definitions, limited cross-herd validation, interoperability gaps and uncertain economics, and many tools still blur the line between detecting animals needing treatment and ancillary predictions. With regulatory and ethical safeguards, progress will depend on reliability-focused standard operating procedures and end-user–oriented design; until then, most systems remain closer to hype than independent clinical technologies.