Diagnozo

Diagnozo is a term used in medical informatics to describe a family of diagnostic frameworks and decision-support tools that aid clinicians in generating and prioritizing differential diagnoses based on patient data. It encompasses software systems, methodologies, and workflows designed to improve diagnostic accuracy and efficiency while preserving clinician oversight.

A typical diagnozo system has three core elements: data ingestion and normalization, a reasoning engine, and a presentation layer. It aggregates structured and unstructured data from electronic health records, labs, imaging, genomics, and patient-reported symptoms; uses probabilistic models, rules, and learned patterns to produce a ranked differential with confidence scores and explanations; and presents results with supporting data.

Deployment and use: Diagnozo tools are intended to fit into clinical workflows and EHR interfaces. They support rapid triage in emergency settings, assist outpatient case reviews, and serve as educational resources. Clinician input remains essential to set priors, verify data, and decide on final diagnoses.

Applications: Neurology (differentials for headache, stroke), oncology (tumor characterization), infectious diseases (fever of unknown origin, sepsis risk), and general internal medicine for complex multisystem presentations. They are also used in research and training to illustrate diagnostic reasoning.

Limitations and considerations: Performance depends on data quality and model validation. Potential biases, overreliance, and interpretability challenges exist, and privacy and governance issues must be addressed. Ongoing evaluation and clinician oversight are essential.

History: The concept emerged from medical AI and decision-support research in the 2010s, evolving with interoperable health IT standards.