Abstract
Diagnostic technologies occupy an important place in modern medicine and health care. They help diagnose diseases, assess the condition of patients, and manage the treatment process. With these technologies, doctors will be able to identify diseases at an early stage, understand their causes and development, and also develop treatment strategies, taking into account the individual characteristics of patients.
References
1. Esteva, A., Kuprel, B., Wang, S., et al. (2019). "Diagnostic Accuracy of Deep Learning Algorithms for Detection of Melanoma in Skin Lesions." JAMA Dermatology, 155(1), 19-26.
2. Gulshan, V., Peng, L., Coram, M., et al. (2016). "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs." JAMA, 316(22), 2402-2410.
3. Rajpurkar, P., Irvin, J., Zhu, K., et al. (2017). "Deep Learning for Chest Radiograph Diagnosis: A Retrospective Comparison of the CheXNeXt Algorithm to Radiologists." PLOS Medicine, 15(11), e1002686.
4. Ting, D. S. W., Cheung, C. Y. L., Lim, G., et al. (2019). "Artificial Intelligence and Deep Learning in Ophthalmology: A Review." The British Journal of Ophthalmology, 103(2), 167-175.
5. Kermany, D. S., Goldbaum, M., Cai, W., et al. (2018). "Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning." Cell, 172(5), 1122-1131.
6. Yang, G., Wang, Y., Zhang, Y., et al. (2020). "Artificial Intelligence in Healthcare: Past, Present, and Future." Seminars in Cancer Biology, 34, 1-10.
7. Choi, E., Schuetz, A., Stewart, W. F., et al. (2016). "Using Deep Learning to Predict Hospital Readmission." Journal of Medical Internet Research, 18(6), e123.