nference language models are able to identify disease warning signs earlier than health care professionals. With our Augmented Curation #AI technology, physicians and researchers can analyze disease signs and symptoms from unstructured clinical text at scale. AL amyloidosis is an excellent example for the value of #AugmentedCuration, as it is frequently misdiagnosed due to its signs and symptoms often mimicking more-common diseases. Also know as primary #amyloidosis, this life-threatening disease results from an abnormality of plasma cells in the bone marrow and while closely related to multiple myeloma, treatment options are far less prevalent. nference collaborated with @JNJInnovMed and the @MayoClinic to study the accuracy of Augmented Curation on over 1,200 AL amyloidosis patients. Our findings demonstrate that the language model-based method matches the quality of manual curation, while being significantly more time-efficient and cost-effective. Going forward, a language model-based method for identifying signs and symptoms from clinical notes could be integrated as part of an AL amyloidosis screening or #EarlyIdentification tool. These tools could reduce the time from the initial presentation of AL amyloidosis to treatment of the disease. Read the peer-reviewed study: doi.org/10.1002/ajh.27019 Request a demo of our AI platform: nference.com/demo #LanguageModels #DiseaseDetection #RealWorldEvidence