Problem representation
A focused summary of relevant features, chronology, context and severity—without flattening important ambiguity.
Reasoning workflow
Clinical reasoning AI
Clinical reasoning AI can help qualified clinicians make assumptions, alternatives and uncertainty more visible. Its safest role is to support a disciplined process—not to make an autonomous diagnosis.
Core workflow
Screen, reason, investigate, verify
A structured cycle reduces the chance that an AI-generated suggestion is mistaken for a conclusion. The clinician controls every transition.
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Screen
Identify instability, time-critical conditions, safeguarding concerns and information that cannot safely wait for a complete work-up.
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Reason
Build a concise problem representation, generate competing hypotheses and identify findings that support, weaken or fail to explain each one.
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Investigate
Select history, examination or tests only when they can meaningfully change probability, management or the need to escalate.
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Verify
Check consequential statements against appropriate sources, reconcile contradictions and document the clinician’s independent assessment.
The cycle repeats: new findings should update the problem representation and differential. A model output is a prompt for review, not an endpoint.
Reasoning structure
What useful AI support should reveal
Competing hypotheses
Several plausible explanations, including dangerous alternatives and non-disease explanations where relevant.
Discriminating findings
The observations that would most change relative probabilities, rather than a long undifferentiated list of questions and tests.
Missing information
Gaps that limit confidence, with no inference that absent documentation means a finding is absent.
Uncertainty
What remains unknown, how sensitive the assessment is to assumptions, and when uncertainty itself warrants review.
Evidence traceability
Links from consequential claims to inspectable evidence, including source date, population and limitations.
Bayesian updating
Revise probability as information arrives
Bayesian reasoning begins with a pre-test probability, expresses it as odds, applies the likelihood ratio of a finding, and converts the result back to a post-test probability.
The calculation
Pre-test odds = probability ÷ (1 − probability)
Post-test odds = pre-test odds × likelihood ratio
Post-test probability = post-test odds ÷ (1 + post-test odds)
Calculate post-test probability with Diagnify’s educational Bayesian tool.
The clinical caveats
- Pre-test probability must fit the patient and setting.
- A likelihood ratio may not transport across populations or methods.
- Correlated findings should not be treated as independent without justification.
- A probability does not set a universal treatment or testing threshold.
Human oversight
The clinician remains accountable for interpretation
Before consulting AI
- Form an initial problem representation.
- Address urgent safety concerns first.
- Understand what data may be missing or unreliable.
- Avoid entering identifiable information without an approved workflow.
After receiving output
- Check for anchoring on the model’s ordering or wording.
- Verify sources and recalculate consequential estimates.
- Compare the output with an independent clinical assessment.
- Escalate when the presentation, uncertainty or stakes require it.
Not autonomous care: clinical reasoning AI can hallucinate, omit crucial diagnoses, misread context and present uncertainty poorly. Diagnify is under evaluation for qualified clinicians and is not intended for emergencies or patient self-diagnosis.
Related guidance
Explore the reasoning stack
Clinician-led by design
Use AI to inspect reasoning, not replace it
Explore Diagnify’s clinical decision-support workflow and review its current limitations before use.