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Ryght AI Secures Accenture Investment to Transform Clinical Research

Partnership aims to accelerate drug development by using agentic AI and digital twins in clinical trials.
Ryght AI Secures Accenture Investment to Transform Clinical Research

Accenture has invested in Ryght AI to modernize clinical research for life sciences companies, leveraging agentic AI to accelerate drug development and reduce trial delays.

Key Highlights

  • Investment Date: December 11, 2025
  • Investor: Accenture Ventures
  • Target: Ryght AI, a platform provider focused on clinical research innovation
  • Goal: Transform how clinical trials are designed and executed, speeding up patient access to new therapies

Why This Matters

Clinical trials are notoriously slow and expensive, with bottlenecks in:
  • Site selection – often manual, adding weeks or months to timelines.
  • Patient enrollment – delays that stall drug development.
By integrating agentic AI (AI systems capable of autonomous decision-making and task execution) with enterprise technology, Ryght AI aims to:
  • Automate site selection and trial design.
  • Improve patient recruitment efficiency.
  • Shorten timelines for bringing treatments to market.

Strategic Impact

  • Life Sciences Companies: Gain tools to manage increasingly complex trials.
  • Patients: Faster access to innovative therapies.
  • Accenture: Strengthens its position in digital health and AI-driven transformation.
Ryght AI also incorporates digital twin technology, creating virtual models of clinical trials to simulate outcomes before execution. This reduces risk and enhances trial design.

Risks & Challenges

  • Data Privacy: Clinical research involves sensitive patient data; AI-driven automation must comply with strict regulations (HIPAA, GDPR).
  • Adoption Curve: Pharma companies may resist shifting from traditional trial methods.
  • AI Reliability: Agentic AI must prove trustworthy in high-stakes medical contexts.
In short: Accenture’s investment in Ryght AI is about using agentic AI to tackle the inefficiencies of clinical trials, combining automation and simulation to accelerate drug development and patient access.
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