Mozu Use cases
From clinical reasoning and documentation to speech, imaging, and care over time, see how AI teams use Mozu data to train and evaluate models on real healthcare work.
Request dataset samplesClinical reasoning
Data for models that need to follow a patient encounter from first question to final decision.
From conversation to diagnosis and treatment
Train models on doctor patient conversation transcripts paired with what the doctor decided next: the diagnosis, the prescription, the referral, and the follow up. Models learn the documented clinical reasoning that connects what a patient says to what a doctor does.
Data: Doctor Patient ConversationsOutcome prediction
Where follow up data is available, teams can study how earlier decisions relate to later events recorded in care.
Data: Doctor Patient ConversationsClinical documentation and administrative work
Data for models and agents that write and act inside patient records.
Visit notes from conversations
Pair each conversation with the note the doctor actually wrote, giving models a target for summarizing a real visit into a clinical note.
Data: Doctor Patient ConversationsPatient record tasks
Tasks grounded in real encounters, such as entering orders, prescriptions, and referrals, and the administrative steps around a visit, for teams building agents that complete work in patient records.
Data: Doctor Patient ConversationsMedical speech and transcription
Data for speech models where clinical details matter.
Medical transcription and speaker attribution
Train and evaluate speech models on real medical conversations with transcripts, speaker diarization, and grading by medical doctors.
Data: Japanese Medical AudioCatching critical errors
Doctor graded transcripts with extra review of medications, dosages, symptoms, and diagnoses, the details where a single transcription error changes the meaning of a visit.
Data: Japanese Medical AudioCare over time
Data for models that need to reason across visits, not one encounter at a time.
Longitudinal psychiatric care
Repeated visits with the same patients, from a detailed intake through regular check ins, so models can learn how symptoms change and why doctors adjust medication and dosage.
Data: Psychiatry Longitudinal EncountersFollow up across encounters
Patients who return for more than one visit let models connect an earlier decision to what the patient reports next.
Data: Doctor Patient ConversationsPatient records and images of conditions
Data for models that combine images, conversations, and records.
Treatment response in dermatology
Before and after images linked to diagnosis, medication, dosage, and transcripts of the patient's visits, so models can connect what a doctor sees and hears to the treatment chosen and assess changes after treatment.
Data: Dermatology Before/After ImagesEvaluations and benchmarks
Tests built around meaningful clinical work, for finding where models fall short.
Clinical evaluations
Use encounter context, documented decisions, and follow up records to test whether models can handle meaningful clinical tasks.
Benchmarks in development
Our benchmark work covers dermatology treatment response and mental health across time.