Artificial Intelligence in Medical Diagnostics
Where AI is being used in medical diagnostics today, what it does well, and the limits clinicians should keep in mind.
AI tools are increasingly used to support diagnosis, especially in specialties built around images. In some narrow, well-defined tasks, studies have reported performance comparable to specialists. But results in research settings don't always carry over to everyday practice, and every tool needs clinical oversight.
Where AI Is Being Used
Image Analysis
- Radiology, such as flagging possible findings on scans for priority review
- Dermatology, such as assessing images of skin lesions
- Pathology, such as reviewing digitized slides
- Ophthalmology, such as screening retinal images for diabetic retinopathy
Most AI tools cleared by the FDA so far are in radiology.
Clinical Decision Support
- Suggesting possible diagnoses to consider
- Checking for drug interactions
- Surfacing guideline-based care protocols
- Estimating a patient's risk of a particular outcome
Predictive Models
- Readmission risk
- Early warning of deterioration, such as sepsis alerts
- Chronic disease progression
Benefits and Limitations
Potential Benefits
✅ Faster triage of urgent findings
✅ A second look that may catch something missed
✅ Less repetitive review work
✅ More consistent results across providers
Limitations
⚠️ Requires human oversight; the clinician stays responsible for the diagnosis
⚠️ Only as good as the data it was trained on
⚠️ Can carry bias against groups under-represented in the training data
⚠️ Performance can drop in a new setting or patient population
⚠️ Too many alerts can lead to alert fatigue
⚠️ Regulatory status and liability are still evolving
Questions to Ask Before Adopting a Diagnostic AI Tool
- What is it cleared or intended for, and what is it not?
- What population was it tested on, and does that match your patients?
- How does it fit into your workflow, and who reviews its output?
- What happens to patient data it processes?
AI in Psychiatry: Documentation, Not Diagnosis
In psychiatry, diagnosis rests on the clinical interview, history and judgment, and the most practical use of AI today is reducing documentation time.
That is where Jogaza focuses. Jogaza's AI scribe drafts documentation; it doesn't make diagnostic suggestions. Diagnosis stays with the clinician. With the patient's agreement to be recorded, the scribe transcribes the visit and drafts a note for the visit type, such as a psychiatric initial evaluation or medication follow-up. It never assigns a suicide or violence risk level and never invents a medication dose; anything not discussed is marked as not discussed. The clinician reviews and edits every draft before saving.