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Using AI to determine optimal cost-effective diagnostic pathways for chronic breathlessness
Max Olsson1, Rafsan Ahmed2, Joakim Ekstrand3
1Lund University, Faculty of Medicine, Department of Clinical Sciences Lund, Respiratory Medicine, Allergology and Palliative Medicine, Lund, Sweden. max.olsson@med.lu.se.
NPJ Primary Care Respiratory Medicine
|August 13, 2026
Summary
An artificial intelligence (AI) model creates cost-effective diagnostic pathways for breathlessness. This AI approach streamlines evaluations, reduces testing, and enables earlier, cheaper diagnoses for patients with breathing difficulties.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Pulmonology
Background:
- Breathlessness is a prevalent clinical symptom with limited evidence for cost-effective diagnostic strategies.
- Identifying the root cause of breathlessness often involves numerous tests, increasing healthcare costs and potentially delaying diagnosis.
Purpose of the Study:
- To develop and validate an artificial intelligence (AI) reinforcement learning model for optimizing low-cost diagnostic pathways for breathlessness.
- To tailor these pathways to specific patient subgroups based on sex and smoking exposure.
Main Methods:
- Utilized Swedish population data of individuals with moderate to severe breathlessness.
- Developed an AI reinforcement learning model incorporating 16 clinically relevant conditions, diagnostic tests, and healthcare costs.
- Defined optimal diagnostic sequences based on cost-effectiveness and diagnostic yield.
Main Results:
- The AI model successfully generated efficient, low-cost diagnostic pathways for breathlessness with high diagnostic yield.
- Optimal pathways were similar across subgroups, prioritizing initial assessments of body mass index, anxiety/depression, physical activity, and spirometry.
- Subsequent steps prioritized lung investigations (e.g., diffusing capacity, CT scans) over cardiac investigations.
Conclusions:
- AI-driven diagnostic pathways can streamline breathlessness evaluation, reducing unnecessary tests and costs.
- This approach facilitates earlier diagnosis and more targeted clinical management for patients experiencing breathlessness.
- The AI model demonstrates potential for improving efficiency and cost-effectiveness in diagnosing the causes of breathlessness.
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