Patients with diverse neurocognitive conditions frequently exhibit measurable language deficits that serve as biomarkers for differential diagnosis and therapy decision making. Discourse analysis can offer reliable ecological measures of human communication, yet manual discourse analysis is cumbersome. Recent advances in automated analysis software provide quick and easy extraction of raw language metrics in the clinic. Nevertheless, transforming these measures into actionable clinical insights remains a significant challenge. The aim of this paper is to present the Automated Language Biomarker Application (ALBA), an integrated framework developed within the Open Brain AI ecosystem to bridge the gap between feature extraction and clinical interpretation. ALBA provides clinicians with a robust statistical infrastructure to benchmark individual patient measures against standardized, large-scale clinical corpora. By utilizing a shared elicitation and processing pipeline, the application ensures that user-provided data are directly comparable to population norms for conditions including Aphasia, Mild Cognitive Impairment (MCI), Dementia, and other neurological conditions. The system implements adaptive statistical logic, employing one-sample t-tests and robust non-parametric alternatives to provide real-time significance testing and dynamic visualizations (box, bar, and violin plots). By automating the comparison of “Language Signatures” against healthy controls and specific clinical phenotypes, ALBA facilitates rapid, evidence-based decision-making in both research and rehabilitation contexts.

Language assessment plays a crucial role in diagnosing and treating individuals with speech, language, and communication disorders caused by neurogenic conditions, whether developmental or acquired. To support clinical assessment and research, we developed Open Brain AI (https://openbrainai.com). This computational platform employs AI techniques, namely machine learning, natural language processing, large language models, and automatic speech-to-text transcription, to automatically analyze multilingual spoken and written productions. This paper discusses the development of Open Brain AI, the AI language processing modules, and the linguistic measurements of discourse macro-structure and micro-structure. The fast and automatic analysis of language alleviates the burden on clinicians, enabling them to streamline their workflow and allocate more time and resources to direct patient care. Open Brain AI is freely accessible, empowering clinicians to conduct critical data analyses and give more attention and resources to other critical aspects of therapy and treatment.