Review  Article •International Open Medical Journal. 1(1):e202502  

Recent Consensus on the Role of AI in Lung Cancer Diagnosis

Authors

Li Zhang1, Wei Liu1,

Affiliations

1 Department of Oncology, Jiangsu Provincial Cancer Hospital, Nanjing, Jiangsu, China

Dear Editor,

Lung cancer remains one of the leading causes of cancer-related mortality globally, and early detection plays a pivotal role in improving patient outcomes. In recent years, artificial intelligence (AI) has emerged as a promising tool for enhancing lung cancer diagnosis, offering improved accuracy and efficiency in detecting malignancies at earlier stages. As we move into 2025, several expert consensus statements have highlighted the critical role AI will play in revolutionizing lung cancer detection and management.

A recent study by Wu et al. (2025) underscores the transformative potential of AI-assisted screening methods, particularly in the context of precision medicine for lung cancer. Their research emphasizes that AI-based algorithms can significantly reduce diagnostic errors and improve personalized risk prediction models (Wu et al., 2025). In line with this, Iqbal et al. (2025) provided an in-depth review of AI’s current and future applications, identifying its potential in enhancing imaging modalities, including CT scans and MRI, which are essential for early-stage detection and evaluation of lung cancer (Iqbal et al., 2025).

Moreover, Kalaluka (2025) highlighted the technical challenges and integration barriers of AI in clinical workflows, pointing out the importance of collaborative efforts to ensure that AI systems are seamlessly integrated into routine practice for lung cancer diagnosis. Their findings suggest that AI tools, when adequately trained on large and diverse datasets, could help in diagnosing not only typical lung cancers but also rare forms, contributing to comprehensive patient management (Kalaluka, 2025).

Despite these advancements, challenges remain in terms of data standardization, system training, and real-world implementation, which need to be addressed to maximize the clinical benefits of AI in lung cancer diagnostics. Further research and validation in large-scale clinical trials are essential to refine AI algorithms and enhance their robustness across diverse populations.

In conclusion, while AI holds immense promise in lung cancer diagnosis, continuous efforts to optimize its application and address existing barriers will be crucial to its successful integration into clinical practice.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.

Statement: During the preparation of this work the author(s) used ChatGpt in order to develop language. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication

 References:

1. Wu, F.Z., Chen, H.H., & Wu, Y.J. (2025). Precision Medicine in Lung Cancer Screening: A Paradigm Shift in Early Detection—Precision Screening for Lung Cancer. Diagnostics, 15(12), 1562. Link
2. Iqbal, R., Sikander, M., Anwar, F., & Fayyaz, R. (2025). AI and Lung Cancer: Basics of AI, Imaging Modalities, Different Tools, Clinical Applications, Challenges & Limitations and Future Directions. Review Journal of Nursing and Medical Sciences. Link
3. Kalaluka, T. (2025). Learning to Diagnose: The Role of AI in Transforming Lung Cancer Detection. Theseus. Link