🤖 AI Triage Bot Diagnostic Accuracy Benchmark Simulator

Assembling a Diagnostic-Concordance Case Set

A representative case panel anchors any fair AI diagnostic benchmark.

• 3: Case categories tracked (common, moderate, rare/atypical)
• 8: Diagnoses per category pool (condition label options)
• Physician: Ground truth source (confirmed final diagnosis)
• 10–200: Panel size range (cases per benchmark run)

Why case-mix matters

A skewed panel toward easy cases inflates apparent accuracy.

Physician-confirmed labels

Each case carries a real physician diagnosis as ground truth.

The AI Triage Bot Suggests a Diagnosis

For every case, the bot outputs one likely underlying condition.

• 1 dx: Inference target (top suggested diagnosis)
• Symptoms: Input signal (patient-reported intake data)
• Label: Output form (single condition name)
• Broad: Scope (not just emergency triage)

Suggestion, not confirmation

The bot proposes a diagnosis without ordering confirmatory tests.

General diagnostic scope

This benchmark spans many conditions, not one binary decision.

Comparing Bot Suggestion to Physician Diagnosis

Each bot label is checked against the matching physician label.

• 1 case: Comparison unit (bot label vs MD label)
• Green: Match outcome (diagnoses agree)
• Red: Mismatch outcome (diagnoses disagree)
• Exact dx: Granularity (not just triage tier)

Exact-match scoring

A match requires the same named condition, not just a category.

Case-by-case visibility

Every case is inspectable, not just the aggregate score.

Calculating the Overall Concordance Rate

Matches divided by total cases gives one headline accuracy number.

• Concordance %: Metric (bot-physician agreement rate)
• ~88%: Typical MD-MD agreement (inter-rater benchmark)
• Stabilizes: Sample size effect (with more cases tested)
• Hides spread: Single number risk (across case difficulty)

One headline number

Overall concordance summarizes performance across the whole panel.

Physician benchmark anchor

Bot rate is compared against typical physician-physician agreement.

Accuracy Benchmark By Case Type

Concordance is split by common, moderate, and rare case categories.

• High: Common-case concordance (bot closely tracks physicians)
• Mid: Moderate-case concordance (noticeable accuracy drop)
• Low: Rare-case concordance (largest bot-physician gap)
• Targeting: Use of breakdown (guides model improvement)

Bar chart by category

Concordance bars reveal exactly where the bot struggles most.

Guiding model improvement

Category gaps point developers toward the weakest condition types.