Psyrin provides frontier AI for behavioral health, automating patient intake and documentation processes to increase clinical efficiency. Their platform, Mira, collects pre-appointment information and generates AI insights to support clinicians before and between patient visits. This technology standardizes assessment quality and reduces administrative burden, improving overall capacity in mental healthcare delivery.
Funding
$1M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Mental healthcare providers face a shortage of clinicians, leading to rushed and inconsistent in-person sessions. Existing diagnostic standards in psychiatry may be outdated, resulting in suboptimal treatment matching and trial-and-error approaches. There is a need for tools that can enhance the quality of care, reduce provider burnout, and redefine psychiatric constructs.
Solution
Psyrin develops AI-powered software that leverages speech-based biomarkers to streamline mental healthcare. The platform analyzes speech patterns to assist in the assessment and monitoring of psychosis and other mental health conditions. Psyrin's tools aim to enhance the quality of care by providing patients with ample time to express their concerns, while also supporting clinical interviews. By automating repeatable clinical work and providing intelligent insights, Psyrin reduces the per-patient workload for clinicians. The company's research focuses on using voice as a biomarker to improve diagnostic accuracy and treatment selection.
Target Audience
Psyrin targets mental healthcare providers, including clinicians and hospitals, as well as government organizations seeking to improve access to and outcomes in mental healthcare.
Features
- AI-powered analysis of speech biomarkers for early detection of mental illness
- Identification of clinical high-risk states and prediction of transition to first-episode psychosis (FEP)
- Prediction of relapse in FEP populations and objective readouts of symptom severity
- Prediction of responders to transcranial magnetic stimulation (TMS)
- Generation of likelihood scores for multiple differential diagnoses
- Identification of symptoms from free speech and two-way dialogue in telehealth appointments