The utility of artificial intelligence in pulmonology has focused mainly on using image datasets to detect and diagnose lung malignancies, but now a growing number of AI models are emerging that apply machine learning to improve predictability for other pulmonary conditions, including pulmonary infections, pulmonary fibrosis, and chronic obstructive pulmonary disease (COPD).
These applications are moving beyond the traditional AI model of collecting data from a multitude of images to cast a wider data net that includes electronic health records.
Also on the horizon, ChatGPT technology is poised to have an impact. But pulmonologists and their practices have a number of barriers to clear before they feel a meaningful impact from AI.
The imperative, said AI researcher Ishanu Chattopadhyay, PhD, is to create transformative models that can detect lung disease early on. Dr. Chattopadhyay, an assistant professor of medicine at the University of Chicago and its Institute for Genomics and Systems Biology, and fellow researchers developed an AI algorithm that uses comorbidity signatures in electronic health records to screen for idiopathic pulmonary fibrosis (IPF) (Nature Med. 2022 Sep 29. doi: 10.1038/s41591-022-02010-y).
“If you could do this when somebody walks into a primary care setting and they are barely suspecting something is going on with them or when they don’t have the typical risk factors, there is a certain fraction of these people who do have IPF and they will almost invariably be diagnosed late and/or misdiagnosed,” Dr. Chattopadhyay said, citing a study that found 55% of patients with IPF have had at least one misdiagnosis and 38% have two or more misdiagnoses (BMC Pulm Med. 2018 Jan 17. doi: 10.1186/s12890-017-0560-x).
Harnessing massive data sets
AI models cull data sets, whether banks of radiographic images or files in an EHR, to extract telltale signatures of a disease state. Dr. Chattopadhyay and his team’s model used three databases with almost 3 million participants and 54,247 positive cases of IPF. Hospitals in Scotland have deployed what they’ve claimed are the first AI models to predict COPD built with 55,000 patient records from a regional National Health Service database. Another AI model for staging COPD, developed by researchers in the United States and Romania, used more than 18,000 medical records from 588 patients to identify physiological signals predictive of COPD (Advanced Sci. 2023 Feb 19. doi: 10.1002/advs.202203485).
Said Dr. Chattopadhyay: “If I can bring in AI which doesn’t just look at radiological images but actually gets it back where someone walks into primary care using only the information that is available at that point in the patient files and asking for nothing more, it raises a flag reliably that gets you a pulmonary referral that will hopefully reduce the misdiagnosis and late diagnosis.”
Dr. Tseng
Victor Tseng, MD, medical director for pulmonology at Ansible Health in Mountain View, Calif., who’s researching the potential of AI in pulmonology, speculated on what functions AI can perform in the clinic. “I think you will start to see much more interventional sort of clinically patient care–facing applications,” he said. That would include acting as a triage layer to direct patient queries to a nurse, physician, or another practitioner, providing patient instructions, serving as therapeutic software, coordinating care, integrating supply chain issues,” he said.