The term "medical diagnosis" in the context of software refers to functions and modules that support healthcare professionals in identifying, classifying, and documenting diseases and health conditions. Such systems process clinical data such as symptoms, findings, laboratory values, imaging data, or vital parameters and provide structured decision support. The aim is to increase diagnostic accuracy, reduce errors, improve adherence to clinical guidelines, and make the diagnostic process more efficient and transparent. The final responsibility for the diagnosis always remains with the treating physician.
Capture and structuring of clinical data: Documentation of symptoms, medical history, physical findings, laboratory values, and vital signs in a structured format.
Guideline- and knowledge-based decision support: Matching patient data with clinical guidelines, scores, and decision trees to support diagnostic decisions.
Algorithmic diagnostic suggestions: Calculating probabilities for specific diseases (e.g., via scoring systems or AI models) and displaying possible differential diagnoses.
Medical image analysis: Assisting in the evaluation of X-ray, CT, MRI, ultrasound, or pathology images, for example by highlighting suspicious structures or performing automatic measurements.
ECG and signal analysis: Automated analysis of ECGs or other biosignals (e.g., EEG, long-term monitoring) with indications of abnormalities.
Risk assessment and early detection: Calculating risk scores (e.g., for cardiovascular disease, sepsis, fall risk) and generating alerts for critical constellations.
Integration with electronic health records and hospital systems: Accessing existing patient data (laboratory, imaging, reports) and writing back diagnoses and codes to HIS/ERP or practice management systems.
Plausibility and consistency checks: Detecting contradictory information or missing mandatory data required for a valid diagnosis.
Coding support (ICD, CPT/OPS, SNOMED CT): Suggesting appropriate diagnosis and procedure codes based on documented findings for billing and statistical analysis.
Telemedical diagnostics: Supporting remote diagnostic workflows, e.g., by enabling upload of images or measurements and providing structured reporting templates.
A radiology application highlights potential pulmonary nodules on CT scans and prioritizes conspicuous cases for radiologist review.
A cardiology information system analyzes ECG data and provides hints on possible arrhythmias, which are then confirmed or rejected by the cardiologist.
A primary care decision support system compares symptoms, lab values, and risk factors with guidelines and presents likely differential diagnoses.
An AI-based dermatology module evaluates images of skin lesions and provides a risk assessment indicating whether specialist follow-up is advisable.
A hospital uses a sepsis early warning system that continuously monitors vital signs and laboratory values and triggers alerts in critical situations.
A diabetology application analyzes continuous glucose monitoring data and identifies patterns that may indicate incorrect insulin dosing or increased hypoglycemia risk.
A neurology system supports the evaluation of MRI and EEG data, for example in the assessment of epilepsy or stroke.