Evaluating AI Software for Healthcare Diagnostics

Chief Medical Information Officer (CMIO)

Where should we be looking for AI software tailored for healthcare diagnostics, and how do we assess it credibly? Our clinicians are being approached directly by vendors, our procurement team has no framework for this, and the published accuracy figures are frankly not believable.

AI Leadership Agent — Clinical AI Advisory

Published accuracy figures are usually true and almost always irrelevant, because they were measured on a curated dataset in a research setting. Four questions matter more than any headline metric, and they should be asked in this order.

The right question is never how accurate the model is. It is how accurate it remains on your patient population, on your imaging equipment, inside your workflow, with your clinicians under time pressure.

1. Regulatory clearance for the exact intended use. A device cleared as a triage aid is not cleared to make a diagnosis. Read the intended-use statement itself, not the marketing summary of it.
2. Population validity. Ask for performance broken down by age, sex, ethnicity, comorbidity and equipment type. If the validation population does not resemble yours, the numbers do not transfer.
3. Workflow performance. Where does the output appear, how long does it take, and what does a clinician do with it? A model that adds thirty seconds per case to a radiology list will be ignored regardless of quality.
4. Post-market surveillance. How does the vendor detect drift, how are you notified, and what is the update process? Diagnostic models degrade as protocols and equipment change.

Which clinical area is under most pressure — imaging backlog, pathology turnaround or something else?

Chief Medical Information Officer (CMIO)

Imaging backlog, and it is severe. Where clinicians push back hardest is accountability. If the software flags a study as normal and it was not, who carries that? Nobody will use a tool that transfers risk onto them.

AI Leadership Agent — Clinical AI Advisory

They are right to insist on an answer, and the answer has to be institutional rather than individual. In every jurisdiction I am aware of, clinical responsibility remains with the clinician, which means the deployment design must protect them.

If a tool increases a clinician's personal liability while increasing the organisation's throughput, it will be quietly abandoned within a year. Deployment design has to make the safe choice the easy one.

1. Deploy first in a role that cannot harm through omission — prioritisation of the worklist rather than exclusion from it. Order changes are far safer than filtering.
2. Never permit auto-discharge on a negative result during initial deployment. Every case still receives human review; the system only changes the order and the attention.
3. Record the model output alongside the clinical decision in the record, including disagreements. This creates both an audit trail and your local validation dataset.
4. State institutional backing explicitly in writing: following or reasonably departing from the tool's suggestion is supported by the organisation. Unwritten reassurance is worthless when something goes wrong.
5. Review local performance quarterly with the clinical team presenting, not the vendor. Ownership of the evidence is what converts scepticism into trust.

Prioritisation is the least glamorous application and the one most likely to survive contact with a real department. Earn trust there before proposing anything more autonomous.