Artificial Intelligence and Machine Learning in Lung Cancer: Advances in Imaging, Detection, and Prognosis
There are still issues with data heterogeneity, interpretability, repeatability, and clinical acceptability despite significant advancements. Standardized datasets, ethical AI implementation, and transparent model evaluation should be the top priorities for future initiatives. AI and ML have revolutionary potential for intelligent, personalized, and real-time lung cancer treatment by connecting computational innovation with precision oncology.
Authors
- Shona Pedersen (ORCID: https://orcid.org/0000-0001-6636-0293)
- Muhammad E. H. Chowdhury (ORCID: https://orcid.org/0000-0003-0744-8206)
- Mohammad Hasan Arshad (ORCID: https://orcid.org/0009-0002-1155-6875)
- Adiba Tabassum Chowdhury (ORCID: https://orcid.org/0009-0001-6363-9018)
- Md. Shaheenur Islam Sumon (ORCID: https://orcid.org/0000-0001-5839-2826)
- Md. Sakib Bin Islam (ORCID: https://orcid.org/0009-0005-6593-495X)
- Zawiyah Sharif (ORCID: https://orcid.org/0009-0009-2063-0804)
- Amshiya Mohammedkasim (ORCID: https://orcid.org/0009-0001-9872-4896)
Institutions
- University of Dhaka (BD)
- Qatar University (QA)
Publication Details
- Journal
- Cancers
- Published
- 2025-12-14
- DOI
- https://doi.org/10.3390/cancers17243985
- Citations
- 1
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.52
Funders
- Qatar University