Geetha Manjunath

Geetha Manjunath

Founder and CEO

NIRAMAI

Dr. Geetha Manjunath is the Founder, CEO and CTO of NIRAMAI Health Analytix, and has led the company to develop a breakthrough AI solution for detecting early stage breast cancer in a non-invasive radiation-free manner. Geetha is a Gold Medalist and PhD holder from Indian Institute of Science with management education from Kellogg Chicago. She comes with over 30 years of experience in IT innovation. Before starting NIRAMAI, Geetha was a Lab Director heading AI Research at Xerox and a Principle Scientist at Hewlett Packard Labs.

Geetha has received many international and national recognition for her innovations and entrepreneurial work, including CSI Gold Medal, BIRAC WinER Award 2018 and is on the Forbes List of Top 20 Self-Made Women 2020. She was the winner of Women Health Innovation Showcase Asia in Singapore, Accenture Vahini Innovator of the Year Award from Economic Times and Women Entrepreneur of the Year 2020 by BioSpectrum India. Geetha is an inventor of 50+ US patents, a senior member of the IEEE and a Fellow of the Indian National Academy of Engineering (INAE).

Artificial Intelligence is transforming medical imaging by enabling faster, more consistent, and often more accurate diagnosis. However, the integration of AI into clinical workflows demands a responsible approach that prioritizes patient safety, fairness, and transparency. This talk will explore the core principles of Responsible AI in medical imaging, including the need for robust validation, bias mitigation, explainability, and data privacy. As a case study, we will examine Thermalytix, an AI-powered breast cancer screening solution and how Responsible AI principles were applied to ensure accuracy, equity, and trust in real-world public health programs. Attendees will gain insights into building and deploying AI systems that not only scale but also uphold the highest standards of ethical healthcare innovation.

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More
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