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BI13.1-5 | Miscellaneous — Part 3

Artificial Intelligence in Clinical Biochemistry Laboratories

This is a rapidly evolving area that is transforming how labs operate. Artificial Intelligence (AI) refers to computer systems that can perform tasks normally requiring human intelligence — pattern recognition, decision-making, and learning from data.

Figure: Artificial Intelligence in Clinical Biochemistry Laboratories

In clinical biochemistry labs, AI is being applied in several ways:

1. Automated Result Validation:
• AI algorithms check whether a lab result is consistent with the patient's history, previous results, and expected biological variation
• They flag implausible results (e.g., a potassium of 9.0 mmol/L in a healthy outpatient) for human review
• This is called autoverification — it reduces the time from sample analysis to result release

2. Quality Control:
• AI detects subtle shifts in analyser performance before they cause reportable errors
• Machine learning models predict when an instrument needs recalibration

3. Pattern Recognition:
• AI can identify patterns in test ordering — flagging unnecessary repeat tests (saving resources)
• Algorithms analyse multiple related results together (e.g., liver function panel + GGT + MCV) to suggest diagnoses like alcoholic liver disease

4. Predictive Analytics:
• Models predict which patients are at risk for sepsis, acute kidney injury, or metabolic emergencies based on trends in their biochemistry results
• Early warning systems alert clinicians before a patient deteriorates

5. Reference Range Optimisation:
• AI helps establish population-specific reference ranges rather than using textbook values
• This is especially relevant in India, where normal ranges may differ from Western populations

Limitations: AI systems require large, high-quality datasets for training. They can perpetuate biases in the data. Human oversight (the "human in the loop") remains essential for patient safety. AI assists the biochemist — it does not replace clinical judgement.