Data from hundreds of thousands of U.S. adults suggests that each zip code increase of 10 µm/m3 in PM2.5 levels is associated with a doubling in eczema rates among residents
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People living in areas with higher levels of air pollution are more likely to have eczema, according to a new study published November 13, 2024 in the open-access journal PLOS ONEby Dr Jeffrey Cohen of Yale School of Medicine, USA.
The prevalence of eczema has increased globally with industrialisation, suggesting a possible contribution from environmental factors. In the new study, researchers used data from the U.S. National Institutes of Health All of Us Research Program, covering hundreds of thousands of U.S. adults. The current study included 286 862 people for whom there was available demographic, zip code and electronic health record data.
Overall, 12 695 participants (4.4%) were diagnosed with eczema. After controlling for demographics and smoking status, people with eczema were more likely to live in zip codes with high levels of fine particulate matter, or PM2.5, in the air. For every increase of 10 µm/m3 in average PM2.5 air pollution in their zip code, people were more than twice as likely to have eczema.
The authors conclude that increased air pollution, as measured by PM2.5, may influence the risk of developing eczema, likely through its effects on the immune system.
The authors add: “Showing that individuals in the United States who are exposed to particulate matter are more likely to have eczema deepens our understanding of the important health implications of ambient air pollution.”
With hospitals already deploying artificial intelligence (AI) to improve patient care, a new study has found that using Chat GPT Plus does not significantly improve the accuracy of doctors’ diagnoses when compared with the use of usual resources.
That said, Chat GPT alone outperformed both groups, suggesting that it still holds promise for improving patient care. Physicians, however, will need more training and experience with the emerging technology to capitalise on its potential, the researchers conclude.
For now, Chat GPT remains best used to augment, rather than replace, human physicians, the researchers say.
“Our study shows that AI alone can be an effective and powerful tool for diagnosis,” said Parsons, who oversees the teaching of clinical skills to medical students at the University of Virginia School of Medicine and co-leads the Clinical Reasoning Research Collaborative. “We were surprised to find that adding a human physician to the mix actually reduced diagnostic accuracy though improved efficiency. These results likely mean that we need formal training in how best to use AI.”
Chat GPT for Disease Diagnosis
Chatbots called “large language models” that produce human-like responses are growing in popularity, and they have shown impressive ability to take patient histories, communicate empathetically and even solve complex medical cases. But, for now, they still require the involvement of a human doctor.
Parsons and his colleagues were eager to determine how the high-tech tool can be used most effectively, so they launched a randomized, controlled trial at three leading-edge hospitals – UVA Health, Stanford and Harvard’s Beth Israel Deaconess Medical Center.
The participating docs made diagnoses for “clinical vignettes” based on real-life patient-care cases. These case studies included details about patients’ histories, physical exams and lab test results. The researchers then scored the results and examined how quickly the two groups made their diagnoses.
The median diagnostic accuracy for the docs using Chat GPT Plus was 76.3%, while the results for the physicians using conventional approaches was 73.7%. The Chat GPT group members reached their diagnoses slightly more quickly overall – 519 seconds compared with 565 seconds.
The researchers were surprised at how well Chat GPT Plus alone performed, with a median diagnostic accuracy of more than 92%. They say this may reflect the prompts used in the study, suggesting that physicians likely will benefit from training on how to use prompts effectively. Alternately, they say, healthcare organisations could purchase predefined prompts to implement in clinical workflow and documentation.
The researchers also caution that Chat GPT Plus likely would fare less well in real life, where many other aspects of clinical reasoning come into play – especially in determining downstream effects of diagnoses and treatment decisions. They’re urging additional studies to assess large language models’ abilities in those areas and are conducting a similar study on management decision-making.
“As AI becomes more embedded in healthcare, it’s essential to understand how we can leverage these tools to improve patient care and the physician experience,” Parsons said. “This study suggests there is much work to be done in terms of optimising our partnership with AI in the clinical environment.”
Following up on this groundbreaking work, the four study sites have also launched a bicoastal AI evaluation network called ARiSE (AI Research and Science Evaluation) to further evaluate GenAI outputs in healthcare. Find out more information at the ARiSE website.
