Imaging
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AI-enabled approaches poised to transform diagnosis, management of lung diseases
Experts explore how AI-based tools can be used to bridge diagnostic challenges and address unmet needs in chronic respiratory disease care.
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AI tools like Sybil poised to improve lung cancer screening, risk prediction
The Sybil model has shown promising results in predicting lung cancer risk in diverse populations from a single CT scan, which could address gaps and disparities in current screening guidelines.
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AI applications in pulmonary medicine: From training clinicians to transforming diagnostics and treatment
AI applications in health care are multiplying, and AI-focused research is gaining traction across specialties, including pulmonary, critical care, and sleep medicine.
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Imaging panel discusses pace, direction of practical AI applications
Amit Gupta, MD, MRMD, CIIP, spoke about the growing application of AI imagery in early-stage lung cancer detection.
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Radiologists, pulmonologists to discuss identification of interstitial lung abnormalities
Joe Mammarappallil, MD, PhD, will chair a Monday afternoon session on the recognition and risk stratification of ILAs, including how to differentiate imaging red herrings from findings that point toward progressive fibrosis.
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Experts to explore imaging modalities for the often-overlooked right heart
Navitha Ramesh, MD, MBBS, FCCP, will chair a session on available imaging techniques and considerations for evaluating the complex structure and function of the right ventricle.
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Updates in ninth edition of TNM classification criteria allow for more precise lung cancer staging
Hisao Asamura, MD, said the changes are critical to clinical decision-making in the evolving thoracic oncology treatment landscape.
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APPs and POCUS: Overcoming credentialing challenges
Advanced practice providers (APPs) play an integral role in the care and management of patients both in the ICU and across the spectrum of health care. Due to reduced residency […]
