Kuidas hindame kliinilise tehisintellekti hallutsinatsiooniriski

Notat'i tõenduslugu ei ole maagiline täpsusprotsent. See on läbipaistev metoodika: eralda kõigepealt kliinilised faktid, genereeri märkmed nendest faktidest, näita toorkonteksti kliinikule ja vaata iga väljund üle vastavalt tõendusmaterjalile.

Hindamismeetod

1. Build the reference facts

A reviewer reads the encounter material and creates a reference set of clinical facts: symptoms, negatives, medication changes, assessment, plan, safety-netting, and coding-relevant details.

2. Compare transcript-direct output

We generate a note from transcript-style input and mark unsupported statements, missing high-salience facts, incorrect attribution, and invented certainty.

3. Compare FactsContext output

We generate documentation from extracted facts and review whether each sentence is supported by a visible fact. The clinician-facing fact list is evaluated as part of the output, not hidden.

4. Record limits, not magic numbers

We do not publish fake accuracy percentages. Each evaluation note includes dataset scope, review date, known limitations, and examples of what the system still requires clinicians to verify.

Mida ülevaatajad märgivad

Unsupported clinical assertion

Wrong medication, dose, frequency, or route

Missing red-flag negative or safety-net advice

Wrong diagnosis certainty: possible vs established

Wrong speaker attribution

Unsupported ICD-10 suggestion

Clinician-visible evidence for each key statement

Facts reusable for notes, codes, referrals, and patient instructions

Kuupäevaga sisemine märge

Praegune hindamismärge, 2026-07-01: see lehekülg dokumenteerib FactsContext arhitektuuri hindamiseks kasutatud meetodi ja kvalitatiivsed näited. See ei väida välist valideerimist ega universaalset täpsust. Järgmine tõendusmileston peaks olema pimestatud, erialade kaupa stratifitseeritud ülevaatus, kus avalikult raporteeritakse andmestiku suurus, ülevaatajate vaheline kokkukõla ja toetuseta väidete määr.

Faktide eraldamine vs otse transkriptist genereerimine

Medication change discussed twice

Otse transkriptist genereerimise risk

“Increase amlodipine to 10 mg and stop lisinopril.” The transcript contained a correction: the clinician first considered stopping lisinopril, then decided to continue it after reviewing renal function.

FactsContext väljund

Facts: amlodipine increased to 10 mg daily; lisinopril continued; renal function normal; review in 6 weeks. Note generated from those facts only.

Miks see oluline on

Transcript-direct generation can smooth over corrections. FactsContext preserves the final decision as a discrete fact before writing.

Negative finding matters

Otse transkriptist genereerimise risk

“No neurological symptoms.” The actual encounter only documented no saddle anesthesia and no bladder symptoms; leg radiation was present.

FactsContext väljund

Facts: left leg radiation to calf; SLR positive left; no saddle anesthesia; no bladder or bowel symptoms. Note keeps the negatives specific.

Miks see oluline on

Broad negative statements are risky. The fact list keeps the clinical context granular and reviewable.

Code suggestion requires evidence

Otse transkriptist genereerimise risk

Suggested J44.1 for COPD exacerbation without showing the symptom or treatment evidence.

FactsContext väljund

Facts: increased breathlessness, purulent sputum, prednisolone burst, antibiotics started. Suggested J44.1 with those facts as evidence.

Miks see oluline on

The code is easier to verify because the reason for the suggestion is visible, not buried in prose.

Teadaolevad piirangud

Clinician review remains mandatory. Notat drafts; clinicians verify and sign.

The method reduces unsupported statements by architecture, but no clinical AI should claim zero hallucinations.

Small internal evaluations are useful for engineering direction, not a substitute for external clinical validation.

Specialty, language, audio quality, speaker overlap, and local coding rules can change performance.

Loe tõendit, seejärel inspekteeri oma fakte.

Hindamismeetod on lihtne, sest toode on loodud inspekteeritavaks: kõigepealt faktid, siis märge, kliiniku ülevaatus alati.

Proovi Notat tasuta