Although LLMs have deep learning capabilities they often fail in criticial decisions like clinical diagnosis because of their high dimensionality (780-1000) in vector space. Till this problem is solved we cannot depend on LLMs for treatment/ diagnosis which can have life and death outcomes.
The useful clinical-AI test here is not whether a model can name bias after the fact. It is whether the eval can hold the case constant, change only the biasing cue, and then show what workflow state would have moved if the answer were trusted.
For surgical or OR-adjacent tools, I would want bias testing tied to action boundaries: did the system alter a readiness claim, routing suggestion, escalation path, or summary tone when the source facts stayed the same?
That makes cognitive bias visible as a product failure, not only a model-behavior finding.
I see the same failure modes daily in retirement planning—clients anchoring on last year's 20% return, or closing their retirement corpus plan at the first 'good enough' number.
Just as these LLMs need matched-pair bias testing, a financial plan is only as good as its stress-test against behavioral pitfalls.
The premium is on building the test, not trusting the model.
Although LLMs have deep learning capabilities they often fail in criticial decisions like clinical diagnosis because of their high dimensionality (780-1000) in vector space. Till this problem is solved we cannot depend on LLMs for treatment/ diagnosis which can have life and death outcomes.
The useful clinical-AI test here is not whether a model can name bias after the fact. It is whether the eval can hold the case constant, change only the biasing cue, and then show what workflow state would have moved if the answer were trusted.
For surgical or OR-adjacent tools, I would want bias testing tied to action boundaries: did the system alter a readiness claim, routing suggestion, escalation path, or summary tone when the source facts stayed the same?
That makes cognitive bias visible as a product failure, not only a model-behavior finding.
I see the same failure modes daily in retirement planning—clients anchoring on last year's 20% return, or closing their retirement corpus plan at the first 'good enough' number.
Just as these LLMs need matched-pair bias testing, a financial plan is only as good as its stress-test against behavioral pitfalls.
The premium is on building the test, not trusting the model.