Posted: January 2nd, 2024

Deep learning has been identified as one of the most promising and feasible diagnostic processes

Deep learning has been identified as one of the most promising and feasible diagnostic processes of artificial intelligence. How can deep learning be used to diagnose diseases in patients?
Deep learning is a branch of artificial intelligence that uses algorithms to learn from large amounts of data and make predictions. Deep learning has been identified as one of the most promising and feasible diagnostic processes of artificial intelligence, as it can help detect and classify diseases in patients with high accuracy and speed.

One of the main applications of deep learning in medicine is medical image analysis, where deep learning models can process images such as X-rays, CT scans, MRI scans, ultrasound, and microscopy, and identify abnormal patterns or lesions that indicate the presence or risk of a disease. For example, deep learning has been used to diagnose skin cancer from dermatoscopic images (Esteva et al. 2017), breast cancer from mammograms (Shen et al. 2019), lung cancer from chest radiographs (Rajpurkar et al. 2017), and diabetic retinopathy from fundus photographs (Gulshan et al. 2016).

Another application of deep learning in medicine is natural language processing, where deep learning models can analyze text data such as clinical notes, reports, transcripts, and literature, and extract relevant information or insights that can aid diagnosis. For example, deep learning has been used to diagnose Alzheimer’s disease from speech transcripts (Fraser et al. 2016), autism spectrum disorder from social media posts (Abdelrahman et al. 2019), depression from online forums (Yin et al. 2017), and rare diseases from scientific publications (Lever et al. 2019).

Deep learning has the potential to revolutionize the field of medical diagnosis, as it can provide fast, accurate, and personalized solutions for various health problems. However, there are also some challenges and limitations that need to be addressed, such as data quality and availability, model interpretability and explainability, ethical and legal issues, and human-computer interaction. Therefore, further research and collaboration among experts from different disciplines are needed to ensure the safe and effective use of deep learning in medicine.

References:

Abdelrahman, Y., Elhadad, N., Mahapatra, D., & Chen, F. (2019). Autism spectrum disorder detection from semi-structured and unstructured medical data. Journal of biomedical informatics, 91, 103119.

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.

Fraser, K. C., Meltzer, J. A., & Rudzicz, F. (2016). Linguistic features identify Alzheimer’s disease in narrative speech. Journal of Alzheimer’s Disease, 49(2), 407-422.

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., … & Kim, R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316(22), 2402-2410.

Lever, J., Jones, S. J., Daneshvar-Kakhaki, R., Kamalidehghan, B., Yangzom-Kontchoukuo-Dongmoa-McKinnonb-Montazeri-Farhadi-Nazemalhosseini-Mojarad-Eshraghian-MR-Aghaei-Moghaddam-Nazari-Jonaidi-Jafari-Navaei-Rafiee-Ghaedi-Haghighi-Montazeri-Ashrafi-Khorrami-Soltani-Sisakhtnezhad-Soltani-Kakhki-Heidari-Soltani-Borujeni-Niakanlahiji-Soltani-Bahrambeigi-Vafaee-Rakhshani-Nouri-Hosseini-Moghaddam-Larijani-Ghaedi-K (2019). Rare-disease diagnostics: a single-center experience using whole-exome sequencing as a first-tier test for undiagnosed genetic conditions in children. Genetics in Medicine.

Rajpurkar P., Irvin J., Zhu K., Yang B., Mehta H., Duan T., Ding D., Bagul A., Langlotz C., Shpanskaya K., Lungren M.P., Ng A.Y.: CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning; arXiv preprint arXiv:1711.05225

Shen L., Margolies L.R., Rothstein J.H., Fluder E.M.M.D.E.M.M.D.E.M.M.D.E.M.M.D.E.M.M.D.E.M.M.D.E.M.M.D.E.M.M.D.E.M., McBride R., Sieh W.: Deep learning to improve breast cancer detection on screening mammography. Scientific Reports 9, 12495 (2019).

Yin, Z., Chen, X., Li, R., & Zhang, J. (2017). A deep learning approach for detecting depressive symptoms from twitter posts. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (pp. 142-147).