New Study on Cross-disciplinary Diagnostics Published in Veterinary Quarterly
Dr Lan’s latest study highlights a cross-disciplinary collaboration with a veterinary science team to develop a machine learning–based diagnostic approach that accelerates disease detection in companion animals. By integrating computational modelling with veterinary oncology expertise, the project analyses patterns in serum biomarkers to distinguish pathological signatures with high sensitivity. The collaborative framework enables rapid screening from minimally invasive samples, supporting earlier and more reliable clinical decisions. This work underscores the value of bridging artificial intelligence and veterinary medicine, and points towards scalable diagnostic tools that may improve outcomes in animal health care.
For more information please refer to https://doi.org/10.1080/01652176.2026.2617470.
