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Potential of AI Hinges on Clinical Validation

The study results show the transformative potential of deep learning in medicine, according to Eduardo Moreno Júdice de Mattos Farina, MD, a neuroradiologist and pediatric radiology fellow at Universidade Federal de São Paulo, and Paulo Eduardo de Aguiar Kuriki, MD, neuroradiologist and assistant professor at UT Southwestern Medical Center in Dallas.

In a commentary accompanying the study, Drs. Farina and Kuriki praised the research for validating a publicly available AI tool in an external population and correlating it with clinical data and relevant outcomes, especially respiratory disease mortality and all-cause mortality.

AI models for prognosis like the one examined in the study can support more personalized health care planning, Dr. Farina noted. For instance, two patients with chronic obstructive pulmonary disease (COPD) of the same age and gender with similar lab results might traditionally receive similar care.

“However, an AI model may reveal differences in mortality risk not apparent through conventional means, prompting clinicians to adjust the frequency or intensity of follow-up accordingly,” he said. “This could help allocate resources more efficiently and improve outcomes through tailored management.”

The key challenge going forward, Dr. Farina said, lies in validating whether model-guided interventions genuinely improve patient outcomes without introducing new biases.

“We need randomized controlled trials where care decisions are based on AI predictions to assess their real-world impact,” Dr. Farina said. “Without this level of evidence, the model’s output risks becoming just another number without clinical consequence.”

Dr. Kim echoed Dr. Farina’s call for more research.

“This study focused on mortality, which is an important but relatively abstract outcome,” he said. “For clinical implementation, further research is needed to link the CXR-Lung-Risk score to more actionable endpoints, such as the incidence of specific respiratory diseases or the impact of targeted interventions.” 

For More Information

Access the Radiology: Artificial Intelligence article, “Predicting Respiratory Disease Mortality Risk Using Open-Source AI on Chest Radiographs in an Asian Health Screening Population,” and the related commentary, “Predicting Mortality with Deep Learning: Are Metrics Alone Enough?”

Read previous RSNA News stories on chest imaging: