Artificial Intelligence in Pulmonary Function Test Interpretation: A New Era in Respiratory Medicine Post

By Campion Quinn, MD

Introduction

Pulmonary function tests (PFTs) have long been a cornerstone of respiratory medicine, helping physicians diagnose, monitor, and manage lung diseases such as asthma, chronic obstructive pulmonary disease (COPD), and interstitial lung disease (ILD). However, the interpretation of PFTs is not always straightforward. Physicians must navigate complex datasets, account for patient variability, and integrate clinical context—all while under time constraints.

Artificial intelligence (AI) is poised to transform this process, offering enhanced accuracy, efficiency, and accessibility in PFT interpretation. But how does AI work in this context? And what does it mean for practicing physicians? This essay explores the role of AI in pulmonary function test interpretation, focusing on its impact on clinical care, administrative efficiency, and patient outcomes.

Understanding AI in Medicine: No Computer Science Degree Required

AI, in simple terms, is a way for computers to learn from large amounts of data and make predictions based on patterns they identify. Imagine training a medical student—over time, they learn to recognize disease patterns from repeated exposure to cases. AI functions similarly but at a much faster and broader scale.

There are different types of AI models used in healthcare:

- Machine Learning (ML): AI systems that improve their accuracy over time by learning from new data.
- Deep Learning: A subset of ML that mimics human brain function, using "neural networks" to recognize patterns in images and numbers.
- Explainable AI (XAI): AI systems that provide reasoning for their conclusions, helping clinicians understand and trust their outputs.

In the context of pulmonary function tests, AI can analyze complex datasets, identify subtle trends, and assist physicians in reaching more precise interpretations.

The Challenges of Traditional PFT Interpretation

Despite their diagnostic value, PFTs pose several challenges:

1. Interobserver Variability: Even experienced pulmonologists may disagree on the classification of PFT results. A study in the European Respiratory Journal found that pulmonologists correctly diagnosed cases from PFTs only 44.6% of the time, with substantial variation between experts.
2. Time Constraints: Physicians often juggle multiple responsibilities, leaving limited time for in-depth PFT analysis.
3. Data Complexity: PFTs contain multiple variables (FVC, FEV₁, DLCO, etc.), which must be interpreted together to form an accurate diagnosis.

AI offers solutions to these challenges by standardizing interpretation, reducing errors, and allowing physicians to focus on patient care rather than data analysis.

AI Applications in PFT Interpretation

### 1. Enhancing Clinical Accuracy
AI models have demonstrated the ability to outperform human pulmonologists in interpreting PFTs. A study comparing AI interpretation with pulmonologists found that AI matched guideline-defined PFT patterns with 100% accuracy and assigned the correct diagnosis in 82% of cases.

### 2. Predicting Pulmonary Function from Imaging
Traditionally, spirometry has been the gold standard for pulmonary function assessment, but AI has introduced a novel approach: estimating lung function directly from chest X-rays.

### 3. AI-Assisted Decision Support for Physicians
Explainable AI (XAI) allows AI systems to not only generate interpretations but also explain their reasoning, improving diagnostic confidence among pulmonologists.

AI’s Impact on Administrative Efficiency

In addition to clinical benefits, AI can significantly reduce administrative burdens in respiratory medicine.

- Automated Report Generation: AI can generate structured PFT reports, reducing documentation time.
- Integration with Electronic Health Records (EHRs): AI can automatically pull relevant patient data, reducing the need for manual entry.
- Triage and Prioritization: AI can flag abnormal results, ensuring that high-risk patients receive timely attention.

By streamlining these processes, AI allows physicians to focus more on patient interaction and personalized care.

Patient Outcomes: A Better Future with AI

AI-driven PFT interpretation is not just about efficiency—it also leads to improved patient care.

1. Early Detection of Respiratory Decline: AI can identify subtle patterns that may indicate early disease progression, allowing for timely intervention.
2. Improved Diagnostic Confidence: When AI supports clinical decision-making, physicians feel more confident in their diagnoses, leading to better treatment decisions.
3. Greater Access to Care: AI models can assist in regions with limited access to pulmonary specialists, ensuring that more patients receive high-quality care.

Ultimately, AI enhances both the physician's diagnostic abilities and the patient's experience in managing lung disease.

Challenges and Considerations in AI Adoption

Despite its promise, AI in pulmonary medicine is not without challenges:

- Data Quality: AI models require high-quality, standardized data to perform optimally.
- Interpretability: Physicians may be hesitant to trust AI-generated results if the reasoning is unclear.
- Regulatory and Ethical Concerns: AI models must be validated to ensure safety, accuracy, and fairness across diverse patient populations.

To address these concerns, ongoing research is focused on making AI more transparent, integrating it seamlessly into clinical workflows, and ensuring regulatory compliance.

Conclusion: Embracing AI as a Partner in Pulmonary Medicine

Artificial intelligence is revolutionizing the interpretation of pulmonary function tests, offering increased accuracy, efficiency, and accessibility. By enhancing diagnostic precision, reducing administrative burdens, and improving patient outcomes, AI serves as an invaluable partner to physicians.

As AI technology continues to evolve, its role in respiratory medicine will expand, offering innovative solutions to long-standing challenges. For physicians, embracing AI is not about replacing clinical expertise but rather augmenting it—ensuring that patients receive the best possible care in an era of data-driven medicine.

The future of pulmonary medicine is here, and AI is leading the way.

References

1. Topalovic, M., Das, N., Burgel, P-R., et al. (2019). Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. European Respiratory Journal, 53(1801660).
2. Ueda, D., Matsumoto, T., Yamamoto, A., et al. (2024). A deep learning-based model to estimate pulmonary function from chest x-rays: Multi-institutional model development and validation study in Japan. Lancet Digital Health, 6, e580–e588.
3. Das, N., Happaerts, S., Gyselinck, I., et al. (2023). Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test