Expert‐Designed Fact Sheets and <scp>AI</scp> ‐Based Analysis of Patient Symptoms to Combat Diagnostic Delays in Inherited Metabolic Diseases

The importance of early diagnosis of inherited metabolic diseases (IMDs) is well known, as it allows early intervention to prevent or reduce complications and improve prognosis, since many of these disorders are treatable. However, diagnosis can still be delayed, and many patients remain undiagnosed. Reducing diagnosis delays is a primary goal of the French Ministry of Health and Prevention (Rare Disease Department). This article describes a national initiative coordinated by the French network for IMD, "Filière G2m." Sixty-seven IMD experts from various reference and competence centers in France drafted one-page summaries dedicated to specific diseases or groups of diseases in the field of IMDs, covering the full spectrum of IMDs. These documents include keywords summarizing clinical signs which, when considered alongside data from routine biological or imaging tests, should suggest the diagnosis of an IMD. A total of 48 summaries have been drafted and are available on the Filière G2m website. To assess the accuracy and relevance of the diagnostic fact sheets, we selected 4 IMDs and compared their content with the clinical profiles of patients followed at Necker-Enfants Malades Hospital, using Natural Language Processing tools to automatically extract patient phenotypes from medical records (Dr Warehouse). We found a strong alignment between the fact sheets and the real-world clinical data from these patients. This tool will enable patients to recognize themselves in an IMD. General practitioners will use these documents alongside diagnostic aid software. It may also support new artificial intelligence-based technologies to identify undiagnosed patients in hospital databases.