These advancements illustrate how rheumatologists, radiologists, computer scientists and others will likely collaborate in the future to make faster and more accurate diagnoses. The future appears bright for the use of artificial intelligence in rheumatology imaging, and this future may be fast approaching. It does not require a great deal of imagination to see how artificial intelligence can buttress human intelligence and help doctors and patients in our quest to think smarter and faster.
Jason Liebowitz, MD, completed his fellowship in rheumatology at Johns Hopkins University, Baltimore, where he also earned his medical degree. He is currently in practice with Skylands Medical Group, N.J.
ad goes here:advert-1
ADVERTISEMENT
SCROLL TO CONTINUE
References
- Deo RC. Machine learning in medicine. Circulation. 2015 Nov 17;132(20):1920–1930.
- Sinha U. First artificial neurons: The McCulloch-Pitts model. AI Shack. 2020.
- Chen J. Neural network. Investopedia. 2020 May 20.
- van der Heijde D. How to read radiographs according to the Sharp/van der Heijde method. J Rheumatol. 2000 Jan;27(1):261–263.
- Ariani A, Silva M, Seletti V, et al. Quantitative chest computed tomography is associated with two prediction models of mortality in interstitial lung disease related to systemic sclerosis. Rheumatology (Oxford). 2017 Jun 1;56(6):922–927.