Emerging Role of Artificial Intelligence in Academic Pulmonary Medicine

Authors: William J. Healy, MD, Ali Musani, MD, David J. Fallaw, MD, Shaheen U. Islam, MD, MPH


A robot may not injure a human being or, through inaction, allow a human being to come to harm. This excerpt from Isaac Asimov’s classic I, Robot fictional narrative is one of the “three laws of robotics” that determined how robots interact with their world.1,2 These words, first printed in 1950, now appear prophetic as we as a profession and more broadly as humans cope to understand how artificial intelligence (AI) fits into the human experience. AI is the ability of machines to complete tasks through mimicking human cognitive functions in applications such as problem solving and learning. It is our perspective that AI will transform the practice of pulmonary medicine.

This content is limited to qualifying members.

Existing members, please login first

If you have an existing account please login now to access this article or view purchase options.

Purchase only this article ($25)

Create a free account, then purchase this article to download or access it online for 24 hours.

Purchase an SMJ online subscription ($75)

Create a free account, then purchase a subscription to get complete access to all articles for a full year.

Purchase a membership plan (fees vary)

Premium members can access all articles plus recieve many more benefits. View all membership plans and benefit packages.


1. European Society of Radiology. What the radiologist should know about artificial intelligence – an ESR white paper. Insights Imaging. 2019;10:44.
2. Asimov I. I, Robot. New York: Bantam Dell; 1950.
3. Luchini C, Pea A, Scarpa A. Artificial intelligence in oncology: current applications and future perspectives. Br J Cancer 2022;126:4–9.
4. Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med 2019;25:954–961.
5. Asfahan S Elhence P, Dutt N, et al. Digital-Rapid On-site Examination in Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration (DEBUT): a proof of concept study for the application of artificial intelligence in the bronchoscopy suite. Eur Respir J 2021;58:2100915.
6. Feng PH, Lin YT, Lo CM. A machine learning texture model for classifying lung cancer subtypes using preliminary bronchoscopic findings. Med Phys 2018;45:5509–5514.
7. Kroth PJ, Morioka-Douglas N, Veres S, et al. The electronic elephant in the room: physicians and the electronic health record. JAMIA Open 2018;1: 49–56.
8. Coiera E, Liu S. Evidence synthesis, digital scribes, and translational challenges for artificial intelligence in healthcare. Cell Rep Med 2022;3: 100860.
9. Gordon M, Viganola D, Bishop M, et al. Are replication rates the same across academic fields? Community forecasts from the DARPA SCORE programme. R Soc Open Sci 2020;7:200566.
10. Bengtsson E, Malm P. Screening for cervical cancer using automated analysis of PAP-smears. Comput Math Methods Med 2014;2014:842037.
11. Magrabi F, Ong M-S, Runciman W, et al. Patient safety problems associated with heathcare information technology: an analysis of adverse events reported to the US Food and Drug Administration. AMIA Annu Symp Proc 2011;2011:853–857.
12. National Conference of State Legislatures. Artificial intelligence 2023 legislation. Updated January 12, 2024. Accessed December 4, 2023.