Transforming healthcare with trustworthy AI

Torsion creates LLM-powered healthcare virtual assistants for appointment scheduling, medication reminders, and patient questions, reducing administrative burdens and enhancing patient satisfaction.

We implement AI to summarize doctor-patient interactions into structured EHR notes, improving accuracy while saving time for care teams.

We help create AI roadmaps to enhance diagnostics, patient engagement, and compliance.

We help create AI roadmaps to enhance diagnostics, patient engagement, and compliance.

Torsion designs AI workflows to automate claims validation and flag discrepancies, reducing processing times and cutting administrative costs.

Using machine learning, we help payers identify anomalies in claims data, preventing fraud and protecting resources.

Torsion integrates automated systems to monitor HIPAA and regulatory compliance, ensuring seamless audits and error-free reports.

Torsion leverages AI to analyze clinician and patient feedback, helping manufacturers refine products and improve adoption.

We build predictive tools to anticipate equipment failures, reducing downtime and ensuring devices remain operational in critical settings.

Torsion connects IoT devices to AI systems, enabling real-time analysis of usage patterns for better patient outcomes and smoother medical device integration.

Torsion partners with pharmaceutical companies to deploy AI models that predict compound efficacy, cutting research timelines.

We use AI to consolidate and interpret trial data, reducing delays and enabling faster, evidence-based decisions.

Torsion builds predictive models that align distribution with demand, minimizing waste and ensuring patient access to vital medications.

Torsion applies AI to analyze complex genomic datasets, helping researchers uncover patterns for precision therapies.

We integrate siloed data into cohesive platforms, speeding up workflows and improving research reliability.

Torsion implements machine learning to evaluate biomarkers, improving preclinical predictions and cutting R&D costs.