Earlier diagnosis plays a crucial role in improving the prognosis of cancer, as delays in starting therapy are associated with decreased survival rates. In most cases, cancer is first identified when symptoms become apparent in general practice, and timely detection in this setting could significantly improve outcomes. Lung cancer, in particular, has one of the poorest prognoses. While several emerging technologies, including new biomarkers, electronic nose technology, and free-circulating tumor DNA, have shown potential for earlier diagnosis in general practice, these have yet to result in widely applicable diagnostic tests. For about 80% of patients diagnosed with lung cancer, the journey begins in general practice (GP). However, approximately 75% of patients are diagnosed at an advanced stage (3 or 4), leading to an 80% mortality rate within one year. The long-term data available in GP records may contain crucial information that could be used to identify patients at risk for cancer at earlier stages. One promising method for improving cancer risk identification involves using the text data available in GP patient records. However, prior attempts to leverage this information have not yielded improved performance over existing clinical prediction tools, likely due to the predefined nature of these predictors. Now, general practitioners may soon be able to identify patients at higher risk of lung cancer up to four months earlier than current practices by using an algorithm during consultations. 

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