Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. Effective strategies for early detection are therefore of paramount importance. In recent years, machine learning (ML) has demonstrated considerable potential in healthcare by facilitating the detection of various diseases. In this retrospective development and validation study, we developed an ML model based on dynamic ensemble selection (DES) for LC detection. The model leverages standard blood sample analysis and smoking history data from a large population at risk in Denmark. The study includes all patients examined on suspicion of LC in the Region of Southern Denmark from 2009 to 2018. We validated and compared the predictions by the DES model with diagnoses provided by five pulmonologists. Among the 38,944 patients, 9,940 had complete data of which 2,505 (25\%) had LC. The DES model achieved an area under the roc curve of 0.77$\pm$0.01, sensitivity of 76.2\%$\pm$2.4\%, specificity of 63.8\%$\pm$2.3\%, positive predictive value of 41.6\%$\pm$1.2\%, and F\textsubscript{1}-score of 53.8\%$\pm$1.1\%. The DES model outperformed all five pulmonologists, achieving a sensitivity 9\% higher than their average. The model identified smoking status, age, total calcium levels, neutrophil count, and lactate dehydrogenase as the most important factors for the detection of LC. The results highlight the successful application of the ML approach in detecting LC, surpassing pulmonologists' performance. Incorporating clinical and laboratory data in future risk assessment models can improve decision-making and facilitate timely referrals.

A Polish startup is using artificial intelligence-powered technology to help parents monitor their children for early signs of respiratory issues. StethoMe, based in the city of Poznań, has developed a smart wireless stethoscope that can detect, classify and analyze pathological sounds within children's lungs through the use of AI. It claims that the technology can increase the accuracy of results and analysis by up to 13%, providing "peace of mind for parents examining their children at home" and "more accurate readings for doctors". "The healthcare challenge we are tackling is the lack of remote auscultation and the poor accuracy and subjectiveness of this kind of examination. There is currently no objective method for diagnosing lung conditions at home," says CEO Wojciech Radomski. "The absence of a solution poses difficulties in the monitoring of chronic respiratory diseases such as asthma, cystic fibrosis, as well as cardiac screening.

The Propeller spirometer and app uses advanced analytics to help patients identify triggers, symptoms, trends and other personalized insights. Also, Propeller's Air is an open API that uses machine learning from Propeller devices and environmental sources and can predict how asthma may be affected by local environmental conditions.

Artificial intelligence can significantly improve the diagnosis of lung disease, suggests a new study. Artificial intelligence (AI) can improve the diagnosis of lung disease by helping doctors interpret respiratory symptoms more accurately, according to new research. An AI computer algorithm using high quality data proved more consistent and accurate in interpreting respiratory test results and suggesting diagnoses than lung specialists, revealed recent research presented at the European Respiratory Society International Congress in Paris, France. "Pulmonary function tests provide an extensive series of numerical outputs and their patterns can be hard for the human eye to perceive and recognise; however, it is easy for computers to manage large quantities of data like these and so we thought AI could be useful for pulmonologists," said Dr Marko Topalovic, a postdoctoral researcher at the Laboratory for Respiratory Diseases, Catholic University of Leuven, Belgium. The study included 120 pulmonologists from 16 hospitals and researchers used historical data from 1,430 patients from 33 Belgian hospitals.

Dr Marko Topalovic (PhD), a postdoctoral researcher at the Laboratory for Respiratory Diseases, Catholic University of Leuven (KU Leuven), Belgium, told the meeting that after training an AI computer algorithm using good quality data, it proved to be more consistent and accurate in interpreting respiratory test results and suggesting diagnoses than lung specialists. "Pulmonary function tests provide an extensive series of numerical outputs and their patterns can be hard for the human eye to perceive and recognise; however, it is easy for computers to manage large quantities of data like these and so we thought AI could be useful for pulmonologists. We explored if this was true with 120 pulmonologists from 16 hospitals. We found that diagnosis by AI was more accurate in twice as many cases as diagnosis by pulmonologists. These results show how AI can serve as a second opinion for pulmonologists when they are assessing and diagnosing their patients," he said.