Employment of artificial intelligence for early lung cancer diagnosis: a retrospective cohort study
These findings provide compelling evidence that AI tools can enhance the follow-up rates for patients with incidentally detected lung nodules and expedite the diagnosis of lung cancer. The integration of AI in clinical practice may significantly improve patient outcomes in lung cancer detection and management.
Authors
- Jasleen Pannu (ORCID: https://orcid.org/0000-0002-4240-9918)
- Vishisht Mehta (ORCID: https://orcid.org/0000-0002-2154-802X)
- Annika Rings (ORCID: https://orcid.org/0000-0002-4065-5330)
- Nathan R. Hill (ORCID: https://orcid.org/0000-0001-7927-8678)
- Travis Dotson (ORCID: https://orcid.org/0000-0002-5093-734X)
- Václav Potěšil
- David P. Carbone (ORCID: https://orcid.org/0000-0003-3002-1921)
- Christina Bellinger (ORCID: https://orcid.org/0000-0002-6648-8251)
- L. Pickup (ORCID: https://orcid.org/0000-0003-3183-297X)
- N. Waterfield Price (ORCID: https://orcid.org/0000-0002-7097-4383)
- Sarah E Maus
Institutions
- Atrium Health Wake Forest Baptist (US)
- The Ohio State University Wexner Medical Center (US)
- Bristol-Myers Squibb (Belgium) (BE)
- Comprehensive Cancer Centers of Nevada (US)
- The Ohio State University (US)
Publication Details
- Journal
- BMJ Open Respiratory Research
- Published
- 2025-11-01
- DOI
- https://doi.org/10.1136/bmjresp-2025-003225
- Citations
- 1
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.52
Funders
- Bristol-Myers Squibb
- Society for Immunotherapy of Cancer