Artificial Intelligence in Healthcare

Annotate | Train | Deploy

Artificial Intelligence in Healthcare

Artificial Intelligence in Healthcare

Annotate | Train | Deploy

Trending

Trending

With the advent of artificial intelligence in healthcare, the industry is witnessing a new wave of AI-fuelled technology innovations delivering reliable performance comparable to human experts.

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.

 

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Opportunities & Challenges

Opportunities & Challenges

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

Artificial Intelligence in Healthcare
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

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