Artificial Intelligence in Healthcare

Annotate | Train | Deploy

Artificial Intelligence in Healthcare

Annotate | Train | Deploy

The increasing utilization of digital technologies in healthcare has led to an explosion of medical data. Businesses are using internal and domain-specific data to build highly accurate AI models for improving their internal processes and adding value to their customer-facing products, respectively while navigating the regulatory requirements.

Technical Whitepaper

Adverse Event Reporting: Power of AI Enabled Cognitive Case Processing and Process Automation

Opportunities & Challenges

Companies willing to adopt AI require access to inter-disciplinary expertise to identify potential areas of opportunity, generate insights, and build meaningful and differentiated solutions.

Data is a primary ingredient for building AI-based solutions. Businesses must have quality annotated data to build highly accurate AI models while having a necessary data security infrastructure in place. Moreover, companies need to have a clear understanding of regulations and best practices to expedite their AI-based developments, e.g. establishing clear expectations on quality systems and good ML practices, conducting a premarket review for AI-based SaMDs, etc.

Service Framework

Engage with Tata Elxsi for

Realizing POCs for rapid innovation
Enhancing diagnostic applications
Building & training AI models
Enabling predictive analytics

Differentiators

Transfer learning capabilities on established AI models such as Inception, VGG, ResNet, etc. for rapid prototyping
Standard QA methods and processes to ensure high-quality annotations of medical data
ISO 27001:2013 certified IT infrastructure for data security
Model lifecycle management adhering to Good Machine Learning Practices (GMLP) and Total Product Lifecycle (TPLC) approach recommended by the FDA

Benefits to the Customer

Delivering high accuracy models extremely close to human experts while significantly reducing episodes of errors and discrepancies
AI-based predictive analytics to proactively monitor and gather insights from patient data, and to create alerts in case of adverse events
Integrated AI-driven healthcare solutions to improve overall efficiency, thereby reducing healthcare cost for the patients