The Promise of AI in Lab Interpretation

A typical veterinary clinic processes dozens of lab reports daily — complete blood counts, biochemistry panels, urinalyses, thyroid panels, and more. Each report contains multiple analytes with reference ranges, and the clinical significance depends on the interplay between values, the patient's signalment, history, and clinical presentation. Interpreting lab results is a cognitively demanding task, and doing it under time pressure increases the risk of oversight.

AI-powered lab analysis tools address this by automatically parsing lab report data, flagging abnormalities, identifying patterns across multiple analytes, and generating plain-language interpretive summaries. The speed is remarkable: what takes a clinician several minutes of careful review can be processed in seconds.

What AI Does Well

Abnormality detection and flagging

AI excels at the mechanical task of comparing each value against reference ranges and highlighting those that fall outside normal limits. But it goes further than simple flagging — it can categorise the severity of deviations (mild, moderate, marked) and group related abnormalities into patterns. For example, elevated BUN and creatinine with concentrated urine specific gravity suggests pre-renal azotaemia, while the same elevations with dilute urine points toward renal disease.

Pattern recognition across analytes

This is where AI provides genuine value beyond what a quick scan can catch. A stress leukogram (mature neutrophilia, lymphopaenia, eosinopenia, monocytosis) is easy to miss when reviewing individual values sequentially. An AI system identifies the pattern instantly and names it, saving the clinician from having to mentally correlate four separate values.

Trend analysis over time

When a patient has serial lab work, AI can plot trends and identify clinically significant changes — even when individual values remain within reference ranges. A creatinine that rises from 1.0 to 1.4 mg/dL over six months is still "normal" by reference range standards, but the trend suggests early renal compromise. AI is particularly good at catching these subtle progressions that are easy to overlook when comparing reports side by side.

Report standardisation

Different laboratories use different report formats, units, and reference ranges. AI normalises this variability, presenting results in a consistent format regardless of which lab processed the sample. This is especially valuable for clinics that use multiple reference laboratories.

Client-facing summaries

AI can generate plain-language summaries suitable for sharing with pet owners — explaining what each abnormality means without medical jargon. This saves the clinician from writing discharge explanations from scratch and helps clients understand why further diagnostics or treatments are recommended.

What AI Cannot Do

Replace clinical context

This is the most critical limitation. Lab values do not exist in isolation. A mildly elevated ALT in an otherwise healthy young dog is a very different clinical picture from the same ALT elevation in a geriatric dog with weight loss, vomiting, and a palpable abdominal mass. AI can flag the abnormality, but it cannot perform the physical exam, take the history, or weigh the full clinical picture.

Current AI tools work primarily with the numbers on the report. They do not know that the patient was dehydrated at the time of sampling (which concentrates values), that the sample was haemolysed (which falsely elevates potassium and LDH), or that the patient is on medications that affect specific analytes.

Handle rare or complex presentations

AI models are trained on common patterns. Atypical presentations — the Addisonian crisis with a normal sodium-to-potassium ratio, the hyperthyroid cat with a T4 in the upper-normal range, the acute pancreatitis with a normal lipase — can confuse or be missed by AI systems that rely heavily on pattern matching. These are precisely the cases where experienced clinical judgement is indispensable.

Account for species-specific nuances

Veterinary medicine spans dozens of species, each with distinct normal values and metabolic quirks. Avian and reptile biochemistry, for instance, differs fundamentally from mammalian biochemistry. While good AI systems are trained on species-specific reference data, the depth of species-specific interpretation varies significantly between products. Always verify that the AI tool you use supports the species you treat.

Make treatment decisions

AI can say "this pattern is consistent with chronic kidney disease, IRIS Stage 2." It cannot say "start this patient on a renal diet, subcutaneous fluids three times weekly, and recheck in four weeks" with the same authority as a clinician who knows the patient, the owner's capabilities, and the practical constraints of the situation. Treatment planning requires a level of contextual reasoning and empathy that remains firmly in the human domain.

Best Practices for Using AI Lab Analysis

Use AI as a first pass, not the final word. Let the AI flag abnormalities and identify patterns, then apply your clinical knowledge to contextualise the findings.
Always review the raw data. Do not rely solely on the AI summary. Glance at the full report to catch anything the AI may have deprioritised or missed.
Input clinical context when possible. If your AI tool allows you to provide signalment, history, or medication information, do so. The more context the model has, the more relevant its analysis.
Be sceptical of "normal" labels. A result within reference range is not necessarily clinically normal for a specific patient. Use serial data and your knowledge of the individual patient to override generic reference range assessments.
Verify species support. Before trusting AI analysis for exotic or uncommon species, confirm that the tool has been validated for that species.
Document the human review. In your medical records, note that AI-assisted analysis was used and that the findings were reviewed and confirmed by the attending veterinarian. This maintains a clear chain of clinical responsibility.

The Practical Impact

When used correctly, AI lab analysis tools deliver real value. They catch abnormalities that might be missed during a rushed review. They identify multi-analyte patterns faster than manual correlation. They provide client-ready explanations that improve communication. And they free up cognitive bandwidth for the clinician to focus on the harder, more nuanced aspects of patient care.

The key is to treat these tools as intelligent assistants — capable and fast, but fundamentally dependent on the clinician for context, judgement, and the final clinical decision. That partnership, rather than wholesale delegation, is where the real benefit lies.