aceUsing AI to determine optimal cost-effective diagnostic pathways for chronic breathlessness
Abstract: 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…
Hospital Universitari Doctor Peset, València 11 by 19Tarrestnom65. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0
On 2026-08-13, researchers reported developing an AI reinforcement learning model using Swedish population data to determine cost-effective diagnostic pathways for chronic breathlessness. The model evaluated 16 clinically relevant conditions with associated tests and costs, generating tailored sequences for subgroups defined by sex and smoking exposure.
The work matters because breathlessness is common but evidence for efficient diagnostic strategies is limited, and the AI approach points to a way to reduce unnecessary testing and lower costs while maintaining high diagnostic yield. Uncertainty remains about performance outside Sweden, in mild breathlessness, and for conditions beyond the 16 included.
- Model was developed using Swedish population data for individuals with moderate to severe breathlessness, tailored to subgroups based on sex and smoking exposure.
- Sixteen clinically relevant conditions were defined along with their diagnostic tests and standard healthcare costs to inform cost-effectiveness.
- Optimal sequences were overall similar between subgroups and prioritized lung investigations ahead of heart investigations.
- Initial steps in AI-derived pathways were clinical evaluations of body mass index, anxiety and depression, physical activity levels, and spirometry.
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.
The rundown
Researchers used Swedish population data for people with moderate to severe breathlessness to train a reinforcement learning model that sequences diagnostic tests by cost and yield, considering 16 conditions and their standard healthcare costs across sex and smoking subgroups.
The resulting pathways started with BMI, anxiety and depression screening, physical activity assessment, and spirometry, followed by diffusing capacity, chest CT, and hemoglobin, with lung-focused investigations ordered before cardiac workup.
Sources
- Peer-reviewednpj Primary Care Respiratory Medicine2026-08-13
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