AI's Role in Improving ILD Diagnosis at Chest 2024

AI's Potential to Transform ILD Diagnosis

Experts at the CHEST 2024 annual meeting in Boston, Massachusetts, explored the role of artificial intelligence (AI) in improving diagnostic accuracy for interstitial lung diseases (ILDs) despite existing limitations.

Understanding AI Techniques in ILD Imaging

During the session titled AI in ILD Imaging: The Future is Here, Michael Morris, MD, associate professor at the University of Arizona, noted that many senior radiologists achieve only about 80% diagnostic accuracy, indicating a significant opportunity for improvement through AI.

• Machine Learning: This technique depends on labeled data to identify key features of lung CT scans, such as honeycombing in advanced ILD cases.
• Deep Learning: Unlike machine learning, this method uses neural networks to analyze data without the need for explicit labels, uncovering patterns autonomously.

Challenges and Limitations of AI

Bruno Hochhegger, MD, PhD, emphasized that AI's deep learning algorithms face challenges, including data bias and technical bias, which could affect ILD diagnosis accuracy. Despite these hurdles, Morris remains optimistic about AI's potential to democratize access to ILD diagnoses among healthcare providers.

Conclusions on AI and ILD

Ultimately, AI is positioned to support radiologists rather than replace them. By integrating clinical information with imaging data, AI can help clinicians make better ILD diagnosis decisions.