SSI Mantra: India’s First Made-in-India Surgical Robot

The SSI Mantra, India’s first indigenously developed surgical robot, has been installed at Noble Hospital in Pune, Maharashtra. Developed in 2017 by Dr. Sudhir Srivastava, this robotic system features five arms and 3D 4K imaging for precise surgical applications. Approved by the Central Drugs Standard Control Organisation (CDSCO), it supports surgeries across general, cardiac, thoracic, urology, and gynecology domains. Offering benefits such as minimally invasive procedures and faster recovery, it also enables telesurgery and teleproctoring, expanding access to remote medical expertise. This innovation represents a significant advancement in India’s healthcare sector.

Premium Content!

You need GS Subscription to access this content. Join now to unlock premium content.

$Machine Learning and Artificial Intelligence Technology Hub (MATH) mind map Recent News Automated math for decision-making under uncertainty Research by MIT Addresses trading off risk and reward Useful across AI Climate Finance Fixes deep learning errors New algorithm aces university math Developed by MIT and others Answers university-level math problems 100% accuracy Enhances machine learning education When Recent advancements Highlighted in 2023 and 2024 Why Improve decision-making in uncertain scenarios Enhance accuracy and efficiency in math education What ADEV tool for probabilistic models Simplifies work with probabilistic models Applied in Climate modeling Financial modeling Operations research e.g., call center queues Machine-learning model for math problems Combines text and code Uses Program synthesis Few-shot learning Over 80% accuracy on unseen problems Where Massachusetts Institute of Technology (MIT) Who Researchers and academics Alex Lew Lead author, MIT PhD student Vikash Mansinghka Co-author, MIT's Probabilistic Computing Project Iddo Drori Project lead, MIT and Columbia University How ADEV tool Automates differentiation of expected values Probabilistic programming Machine learning model for math Pretrained on text and code Fine-tuned on code (Codex by OpenAI) Significance Facilitates experimental work with probabilistic models Opens new possibilities in AI and related fields Significantly improves learning and automation in education Challenges ADEV Perceived complexity of probabilistic models Machine learning model for math Limitations in visual components and computational complexity Way Forward Expand application areas Overcome current limitations and challenges Explore further integration into education and research$