BioticsAI develops an AI-powered platform that enhances fetal ultrasound screenings by validating image quality, localizing anatomical vulnerabilities, and automating report generation. This technology addresses high misdiagnosis rates in fetal malformations, improving patient care and reducing the administrative burden on obstetricians.
Funding
$20K 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
Fetal ultrasound screenings often suffer from high misdiagnosis rates of fetal malformations due to operator errors and variability in image quality. This can lead to increased risk for morbidity and mortality for both the mother and baby. The global shortage of obstetrics professionals further exacerbates these challenges, limiting the time doctors can spend with each patient.
Solution
BioticsAI offers a clinical intelligence platform that leverages AI to enhance the accuracy and efficiency of fetal anomaly screenings. The platform validates the completeness and quality of ultrasound images, localizes fetal vulnerabilities, and automates report generation. By providing real-time feedback to clinicians and improving the quality of documentation, BioticsAI aims to reduce misdiagnosis rates, improve patient experiences, and alleviate the administrative burden on obstetricians. The AI-powered software ensures that patients and their providers have comprehensive information for a healthy pregnancy journey.
Target Audience
The primary target audience includes obstetricians, maternal-fetal medicine specialists, and other healthcare providers involved in prenatal care who seek to improve the accuracy and efficiency of fetal ultrasound screenings.
Features
- AI-driven validation of fetal anomaly screening completeness and quality
- Computer vision for autonomous quality assurance and abnormality localization
- Automated generation of high-quality documentation and reports
- Real-time feedback to clinicians during ultrasound screenings
- Integration of imaging, lab results, and patient history for holistic understanding
- Predictive machine learning models for Placenta Accreta Spectrum