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TRV-2026-0789Certified recordPeer-reviewed

Using AI to determine optimal cost-effective diagnostic pathways for chronic breathlessness

Breathlessness is a common symptom in clinical practice, yet evidence for cost-effective strategies to diagnose the underlying health conditions causing breathlessness remains limited. Using Swedish population data with individuals with moderate to severe breathlessness, we developed an artificial intelligence (AI) reinforcement learning model to identify optimal, low-cost diagnostic pathways for breathlessness tailored to subgroups based on sex and smoking exposure. Sixteen clinically relevant conditions were d…

Health · G Space — documented gain · certified 2026-08-16 · v1 · article view · machine-readable

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An AI reinforcement learning model trained on Swedish population data produced efficient, low-cost diagnostic sequences for moderate to severe breathlessness with high diagnostic yield, prioritizing BMI, anxiety/depression, physical activity, and spirometry before lung diffusion, CT, and hemoglobin tests.

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Findings are based on Swedish data for moderate to severe breathlessness and a defined set of 16 conditions, which may limit generalizability to other populations, severity levels, or causes of breathlessness.

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Truvace Impact Record TRV-2026-0789, v1: “Using AI to determine optimal cost-effective diagnostic pathways for chronic breathlessness.” Truvace, 2026-08-16. /record/TRV-2026-0789 (accessed at citation time). sha256 c5c4703299998534…

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