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VIENNA — Artificial Intelligence (AI) can assist doctors in assessing and diagnosing respiratory illnesses in infants and children, according to two new studies presented at the European Respiratory Society (ERS) 2024 Congress.
Researchers can train artificial neural networks (ANNs) to detect lung disease in premature babies by analyzing their breathing patterns while they sleep. “Our noninvasive test is less distressing for the baby and their parents, meaning they can access treatment more quickly, and may also be relevant for their long-term prognosis,” said Edgar Delgado-Eckert, PhD, adjunct professor in the Department of Biomedical Engineering at The University of Basel, Switzerland, and a research group leader at the University Children’s Hospital, Switzerland.
Manjith Narayanan, MD, a consultant in pediatric pulmonology at the Royal Hospital for Children and Young People, Edinburgh, and honorary senior clinical lecturer at The University of Edinburgh, United Kingdom, said chatbots such as ChatGPT, Bard, and Bing can perform as well as or better than trainee doctors when assessing children with respiratory issues. He said chatbots could triage patients more quickly and ease pressure on health services.
Chatbots Show Promise in Triage of Pediatric Respiratory Illnesses
Researchers at The University of Edinburgh provided 10 trainee doctors with less than 4 months of clinical experience in pediatrics with clinical scenarios that covered topics such as cystic fibrosis, asthma, sleep-disordered breathing, breathlessness, chest infections, or no obvious diagnosis.
The trainee doctors had 1 hour to use the internet, although they were not allowed to use chatbots to solve each scenario with a descriptive answer.
Each scenario was also presented to the three large language models (LLMs): OpenAI’s ChatGPT, Google’s Bard, and Microsoft’s Bing.
Six pediatric respiratory experts assessed all responses, scoring correctness, comprehensiveness, usefulness, plausibility, and coherence on a scale of 0-9. They were also asked to say whether they thought a human or a chatbot generated each response.
ChatGPT scored an average of 7 out of 9 overall and was believed to be more human-like than responses from the other chatbots. Bard scored an average of 6 out of 9 and was more “coherent” than trainee doctors, but in other respects, it was no better or worse than trainee doctors. Bing and trainee doctors scored an average of 4 out of 9.
“Our study is the first, to our knowledge, to test LLMs against trainee doctors in situations that reflect real-life clinical practice,” Narayanan said. “We did this by allowing the trainee doctors to have full access to resources available on the internet, as they would in real life. This moves the focus away from testing memory, where LLMs have a clear advantage.”
Narayanan said that these models could help nurses, trainee doctors, and primary care physicians triage patients quickly and assist medical professionals in their studies by summarizing their thought processes. “The key word, though, is “assist.” They cannot replace conventional medical training yet,” he told Medscape Medical News.
The researchers found no obvious hallucinations — seemingly made-up information — with any of the three LLMs. Still, Narayanan said, “We need to be aware of this possibility and build mitigations.”
Hilary Pinnock, ERS education council chair and professor of primary care respiratory medicine at The University of Edinburgh who was not involved in the research, said seeing how widely available AI tools can provide solutions to complex cases of respiratory illness in children is exciting and worrying at the same time. “It certainly points the way to a brave new world of AI-supported care.”
“However, before we start to use AI in routine clinical practice, we need to be confident that it will not create errors either through ‘hallucinating’ fake information or because it has been trained on data that does not equitably represent the population we serve,” she said.
AI Predicts Lung Disease in Premature Babies
Identifying bronchopulmonary dysplasia (BPD) in premature babies remains a challenge. Lung function tests usually require blowing out on request, which is a task babies cannot perform. Current techniques require sophisticated equipment to measure an infant’s lung ventilation characteristics, so doctors usually diagnose BPD by the presence of its leading causes, prematurity and the need for respiratory support.
Researchers at the University of Basel in Switzerland trained an ANN model to predict BPD in premature babies.
The team studied a group of 139 full-term and 190 premature infants who had been assessed for BPD, recording their breathing for 10 minutes while they slept. For each baby, 100 consecutive regular breaths, carefully inspected to exclude sighs or other artifacts, were used to train, validate, and test an ANN called a Long Short-Term Memory model (LSTM), which is particularly effective at classifying sequential data such as tidal breathing.
Researchers used 60% of the data to teach the network how to recognize BPD, 20% to validate the model, and then fed the remaining 20% of the data to the model to see if it could correctly identify those babies with BPD.
The LSTM model classified a series of flow values in the unseen test data set as belonging to a patient diagnosed with BPD or not with 96% accuracy.
“Until recently, this need for large amounts of data has hindered efforts to create accurate models for lung disease in infants because it is so difficult to assess their lung function,” Delgado-Eckert said. “Our research delivers, for the first time, a comprehensive way of analyzing infants’ breathing and allows us to detect which babies have BPD as early as 1 month of corrected age.”
The study presented by Delgado-Eckert received funding from the Swiss National Science Foundation. Narayanan and Pinnock reported no relevant financial relationships.
A version of this article appeared on Medscape.com.
VIENNA — Artificial Intelligence (AI) can assist doctors in assessing and diagnosing respiratory illnesses in infants and children, according to two new studies presented at the European Respiratory Society (ERS) 2024 Congress.
Researchers can train artificial neural networks (ANNs) to detect lung disease in premature babies by analyzing their breathing patterns while they sleep. “Our noninvasive test is less distressing for the baby and their parents, meaning they can access treatment more quickly, and may also be relevant for their long-term prognosis,” said Edgar Delgado-Eckert, PhD, adjunct professor in the Department of Biomedical Engineering at The University of Basel, Switzerland, and a research group leader at the University Children’s Hospital, Switzerland.
Manjith Narayanan, MD, a consultant in pediatric pulmonology at the Royal Hospital for Children and Young People, Edinburgh, and honorary senior clinical lecturer at The University of Edinburgh, United Kingdom, said chatbots such as ChatGPT, Bard, and Bing can perform as well as or better than trainee doctors when assessing children with respiratory issues. He said chatbots could triage patients more quickly and ease pressure on health services.
Chatbots Show Promise in Triage of Pediatric Respiratory Illnesses
Researchers at The University of Edinburgh provided 10 trainee doctors with less than 4 months of clinical experience in pediatrics with clinical scenarios that covered topics such as cystic fibrosis, asthma, sleep-disordered breathing, breathlessness, chest infections, or no obvious diagnosis.
The trainee doctors had 1 hour to use the internet, although they were not allowed to use chatbots to solve each scenario with a descriptive answer.
Each scenario was also presented to the three large language models (LLMs): OpenAI’s ChatGPT, Google’s Bard, and Microsoft’s Bing.
Six pediatric respiratory experts assessed all responses, scoring correctness, comprehensiveness, usefulness, plausibility, and coherence on a scale of 0-9. They were also asked to say whether they thought a human or a chatbot generated each response.
ChatGPT scored an average of 7 out of 9 overall and was believed to be more human-like than responses from the other chatbots. Bard scored an average of 6 out of 9 and was more “coherent” than trainee doctors, but in other respects, it was no better or worse than trainee doctors. Bing and trainee doctors scored an average of 4 out of 9.
“Our study is the first, to our knowledge, to test LLMs against trainee doctors in situations that reflect real-life clinical practice,” Narayanan said. “We did this by allowing the trainee doctors to have full access to resources available on the internet, as they would in real life. This moves the focus away from testing memory, where LLMs have a clear advantage.”
Narayanan said that these models could help nurses, trainee doctors, and primary care physicians triage patients quickly and assist medical professionals in their studies by summarizing their thought processes. “The key word, though, is “assist.” They cannot replace conventional medical training yet,” he told Medscape Medical News.
The researchers found no obvious hallucinations — seemingly made-up information — with any of the three LLMs. Still, Narayanan said, “We need to be aware of this possibility and build mitigations.”
Hilary Pinnock, ERS education council chair and professor of primary care respiratory medicine at The University of Edinburgh who was not involved in the research, said seeing how widely available AI tools can provide solutions to complex cases of respiratory illness in children is exciting and worrying at the same time. “It certainly points the way to a brave new world of AI-supported care.”
“However, before we start to use AI in routine clinical practice, we need to be confident that it will not create errors either through ‘hallucinating’ fake information or because it has been trained on data that does not equitably represent the population we serve,” she said.
AI Predicts Lung Disease in Premature Babies
Identifying bronchopulmonary dysplasia (BPD) in premature babies remains a challenge. Lung function tests usually require blowing out on request, which is a task babies cannot perform. Current techniques require sophisticated equipment to measure an infant’s lung ventilation characteristics, so doctors usually diagnose BPD by the presence of its leading causes, prematurity and the need for respiratory support.
Researchers at the University of Basel in Switzerland trained an ANN model to predict BPD in premature babies.
The team studied a group of 139 full-term and 190 premature infants who had been assessed for BPD, recording their breathing for 10 minutes while they slept. For each baby, 100 consecutive regular breaths, carefully inspected to exclude sighs or other artifacts, were used to train, validate, and test an ANN called a Long Short-Term Memory model (LSTM), which is particularly effective at classifying sequential data such as tidal breathing.
Researchers used 60% of the data to teach the network how to recognize BPD, 20% to validate the model, and then fed the remaining 20% of the data to the model to see if it could correctly identify those babies with BPD.
The LSTM model classified a series of flow values in the unseen test data set as belonging to a patient diagnosed with BPD or not with 96% accuracy.
“Until recently, this need for large amounts of data has hindered efforts to create accurate models for lung disease in infants because it is so difficult to assess their lung function,” Delgado-Eckert said. “Our research delivers, for the first time, a comprehensive way of analyzing infants’ breathing and allows us to detect which babies have BPD as early as 1 month of corrected age.”
The study presented by Delgado-Eckert received funding from the Swiss National Science Foundation. Narayanan and Pinnock reported no relevant financial relationships.
A version of this article appeared on Medscape.com.
VIENNA — Artificial Intelligence (AI) can assist doctors in assessing and diagnosing respiratory illnesses in infants and children, according to two new studies presented at the European Respiratory Society (ERS) 2024 Congress.
Researchers can train artificial neural networks (ANNs) to detect lung disease in premature babies by analyzing their breathing patterns while they sleep. “Our noninvasive test is less distressing for the baby and their parents, meaning they can access treatment more quickly, and may also be relevant for their long-term prognosis,” said Edgar Delgado-Eckert, PhD, adjunct professor in the Department of Biomedical Engineering at The University of Basel, Switzerland, and a research group leader at the University Children’s Hospital, Switzerland.
Manjith Narayanan, MD, a consultant in pediatric pulmonology at the Royal Hospital for Children and Young People, Edinburgh, and honorary senior clinical lecturer at The University of Edinburgh, United Kingdom, said chatbots such as ChatGPT, Bard, and Bing can perform as well as or better than trainee doctors when assessing children with respiratory issues. He said chatbots could triage patients more quickly and ease pressure on health services.
Chatbots Show Promise in Triage of Pediatric Respiratory Illnesses
Researchers at The University of Edinburgh provided 10 trainee doctors with less than 4 months of clinical experience in pediatrics with clinical scenarios that covered topics such as cystic fibrosis, asthma, sleep-disordered breathing, breathlessness, chest infections, or no obvious diagnosis.
The trainee doctors had 1 hour to use the internet, although they were not allowed to use chatbots to solve each scenario with a descriptive answer.
Each scenario was also presented to the three large language models (LLMs): OpenAI’s ChatGPT, Google’s Bard, and Microsoft’s Bing.
Six pediatric respiratory experts assessed all responses, scoring correctness, comprehensiveness, usefulness, plausibility, and coherence on a scale of 0-9. They were also asked to say whether they thought a human or a chatbot generated each response.
ChatGPT scored an average of 7 out of 9 overall and was believed to be more human-like than responses from the other chatbots. Bard scored an average of 6 out of 9 and was more “coherent” than trainee doctors, but in other respects, it was no better or worse than trainee doctors. Bing and trainee doctors scored an average of 4 out of 9.
“Our study is the first, to our knowledge, to test LLMs against trainee doctors in situations that reflect real-life clinical practice,” Narayanan said. “We did this by allowing the trainee doctors to have full access to resources available on the internet, as they would in real life. This moves the focus away from testing memory, where LLMs have a clear advantage.”
Narayanan said that these models could help nurses, trainee doctors, and primary care physicians triage patients quickly and assist medical professionals in their studies by summarizing their thought processes. “The key word, though, is “assist.” They cannot replace conventional medical training yet,” he told Medscape Medical News.
The researchers found no obvious hallucinations — seemingly made-up information — with any of the three LLMs. Still, Narayanan said, “We need to be aware of this possibility and build mitigations.”
Hilary Pinnock, ERS education council chair and professor of primary care respiratory medicine at The University of Edinburgh who was not involved in the research, said seeing how widely available AI tools can provide solutions to complex cases of respiratory illness in children is exciting and worrying at the same time. “It certainly points the way to a brave new world of AI-supported care.”
“However, before we start to use AI in routine clinical practice, we need to be confident that it will not create errors either through ‘hallucinating’ fake information or because it has been trained on data that does not equitably represent the population we serve,” she said.
AI Predicts Lung Disease in Premature Babies
Identifying bronchopulmonary dysplasia (BPD) in premature babies remains a challenge. Lung function tests usually require blowing out on request, which is a task babies cannot perform. Current techniques require sophisticated equipment to measure an infant’s lung ventilation characteristics, so doctors usually diagnose BPD by the presence of its leading causes, prematurity and the need for respiratory support.
Researchers at the University of Basel in Switzerland trained an ANN model to predict BPD in premature babies.
The team studied a group of 139 full-term and 190 premature infants who had been assessed for BPD, recording their breathing for 10 minutes while they slept. For each baby, 100 consecutive regular breaths, carefully inspected to exclude sighs or other artifacts, were used to train, validate, and test an ANN called a Long Short-Term Memory model (LSTM), which is particularly effective at classifying sequential data such as tidal breathing.
Researchers used 60% of the data to teach the network how to recognize BPD, 20% to validate the model, and then fed the remaining 20% of the data to the model to see if it could correctly identify those babies with BPD.
The LSTM model classified a series of flow values in the unseen test data set as belonging to a patient diagnosed with BPD or not with 96% accuracy.
“Until recently, this need for large amounts of data has hindered efforts to create accurate models for lung disease in infants because it is so difficult to assess their lung function,” Delgado-Eckert said. “Our research delivers, for the first time, a comprehensive way of analyzing infants’ breathing and allows us to detect which babies have BPD as early as 1 month of corrected age.”
The study presented by Delgado-Eckert received funding from the Swiss National Science Foundation. Narayanan and Pinnock reported no relevant financial relationships.
A version of this article appeared on Medscape.com.
FROM ERS 2024