Balu Bhasuran

Abstract Laboratory tests are crucial for diagnosing and managing health conditions, offering essential reference ranges for result interpretation. The diversity of lab tests, influenced by variables such as the specimen type (e.g., blood, urine), gender, age, and other influencing factors like pregnancy, makes automated interpretation challenging.

Abstract We aimed at evaluating the consistency in causal reasoning capabilities of large language models (LLMs) in disease-specific contexts, across the three levels of the causal ladder—associational, interventional, and counterfactual. We aimed at obtaining realistic clinical scenarios involving drug interactions and side effects for Human Immunodeficiency Virus (HIV) antiretroviral therapy (ART).

Abstract This study evaluates causal reasoning in large language models (LLMs) using 99 clinically grounded laboratory test scenarios mapped to Pearl’s Ladder of Causation: association, intervention, and counterfactual reasoning. We focused on common lab tests such as Hemoglobin A1c (HbA1c), creatinine, and vitamin D, and paired them with clinically relevant causal factors, including age, gender, obesity, and smoking.