Generative AI for Advances in Biomedicine
DOI:
https://doi.org/10.71222/7dmwdj95Keywords:
generative AI, medical imaging, structured medical reports, auxiliary diagnosis, clinical decision supportAbstract
The rapid advancement of Generative Artificial Intelligence (GenAI) has sparked widespread interest in the field of medical imaging, revealing unprecedented potential in diverse biomedical applications. Recent developments have enabled GenAI systems to perform at a level comparable to experienced clinicians, capable of generating highly accurate and structured medical reports, providing auxiliary diagnostic suggestions, and supporting complex clinical decision-making processes. By automating routine documentation, analyzing imaging data, and integrating multimodal patient information, GenAI can significantly reduce physician workload, minimize diagnostic errors, and improve the efficiency and clarity of doctor-patient communication. Beyond immediate clinical utility, these technologies hold promise for personalized medicine, predictive diagnostics, and large-scale epidemiological studies, offering transformative opportunities to enhance healthcare delivery and optimize clinical workflows. As research continues, the integration of GenAI into standard medical practice may redefine the role of clinicians, promoting more informed, data-driven, and patient-centered care.
References
1. R. Bhayana, S. Krishna, and R. R. Bleakney, "Performance of ChatGPT on a radiology board-style examination: insights into current strengths and limitations," Radiology, vol. 307, no. 5, p. e230582, 2023. doi: 10.1148/radiol.230582.
2. J. H. Chen, Y. Q. Zhang, T. T. Zhu, Q. Zhang, A. X. Zhao, and Y. Huang, "Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five con-trast-enhanced ultrasound key frames," Frontiers in Endocrinology, vol. 15, p. 1299686, 2024.
3. K. Kim, K. Cho, R. Jang, S. Kyung, S. Lee, S. Ham, and N. Kim, "Updated primer on generative artificial intelligence and large language models in medical imaging for medical professionals," Korean Journal of Radiology, vol. 25, no. 3, p. 224, 2024. doi: 10.3348/kjr.2023.0818.
4. R. Luo, L. Sun, Y. Xia, T. Qin, S. Zhang, H. Poon, and T. Y. Liu, "BioGPT: generative pre-trained transformer for biomedical text generation and mining," Briefings in bioinformatics, vol. 23, no. 6, p. bbac409, 2022.
5. H. Chen, F. J. King, B. Zhou, Y. Wang, C. J. Canedy, J. Hayashi, and Y. Zhou, "Drug target prediction through deep learning functional representation of gene signatures," Nature communications, vol. 15, no. 1, p. 1853, 2024. doi: 10.1038/s41467-024-46089-y.
6. F. Ren, A. Aliper, J. Chen, H. Zhao, S. Rao, C. Kuppe, and A. Zhavoronkov, "A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models," Nature Biotechnology, vol. 43, no. 1, pp. 63-75, 2025. doi: 10.1038/s41587-024-02143-0.
7. S. Harrer, P. Shah, B. Antony, and J. Hu, "Artificial intelligence for clinical trial design," Trends in pharmacological sciences, vol. 40, no. 8, pp. 577-591, 2019. doi: 10.1016/j.tips.2019.05.005.
8. Q. Jin, Z. Wang, C. S. Floudas, F. Chen, C. Gong, D. Bracken-Clarke, and Z. Lu, "Matching patients to clinical trials with large language models," Nature communications, vol. 15, no. 1, p. 9074, 2024. doi: 10.1038/s41467-024-53081-z.
9. W. H. Pinaya, M. S. Graham, E. Kerfoot, P. D. Tudosiu, J. Dafflon, V. Fernandez, and M. J. Cardoso, "Generative ai for medical imaging: extending the monai framework," arXiv preprint arXiv:2307.15208, 2023.
10. M. Mason, Y. Cho, J. Rayo, Y. Gong, M. Harris, and Y. Jiang, "Technologies for medication adherence monitoring and technology assessment criteria: narrative review," JMIR mHealth and uHealth, vol. 10, no. 3, p. e35157, 2022. doi: 10.2196/35157.
11. E. H. Weissler, T. Naumann, T. Andersson, R. Ranganath, O. Elemento, Y. Luo, and M. Ghassemi, "The role of machine learning in clinical research: transforming the future of evidence generation," Trials, vol. 22, no. 1, p. 537, 2021. doi: 10.1186/s13063-021-05489-x.
12. L. Nayak, A. Dasgupta, R. Das, K. Ghosh, and R. K. De, "Computational neuroscience and neuroinformatics: Recent progress and resources," Journal of biosciences, vol. 43, no. 5, pp. 1037-1054, 2018. doi: 10.1007/s12038-018-9813-y.
13. A. B. Ajiboye, F. R. Willett, D. R. Young, W. D. Memberg, B. A. Murphy, J. P. Miller, and R. F. Kirsch, "Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration," The Lancet, vol. 389, no. 10081, pp. 1821-1830, 2017. doi: 10.1016/s0140-6736(17)30601-3.
14. K. R. Wan, T. Maszczyk, A. A. Q. See, J. Dauwels, and N. K. K. King, "A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson's disease," Clinical Neurophysiology, vol. 130, no. 1, pp. 145-154, 2019. doi: 10.1016/j.clinph.2018.09.018.
15. M. Salvagno, F. S. Taccone, and A. G. Gerli, "Can artificial intelligence help for scientific writing?," Critical care, vol. 27, no. 1, p. 75, 2023. doi: 10.1186/s13054-023-04380-2.
