APPLICATIONS OF AI/ML FOR THE HEALTHCARE, KPR Institute Engineering and Technology, Autonomous Engineering Institution, Coimbatore, India

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Offline (Outside the campus)
APPLICATIONS OF AI/ML FOR THE HEALTHCARE
Webinar Club|Society
DATE
Sep 24, 2022
TIME
11:00 AM to 12:00 PM
DEPARTMENT
Biomedical Engineering Society of India
TOTAL PARTICIPATES
170
Outcome
Students able to acquire new technologies of AI/ML in Healthcare such as
AI in drug discovery
AI/ML in personalized medicine
AI/ML in robotic surgeries
Summary
The Department of Biomedical Engineering in association with the BMESI student chapter organized a webinar on ‘Applications of AI/ML for the Healthcare’ on 24.09.2022 at 11.00 pm to 12.00 noon. The guest of honour by Dr. B. Surendiran, Associate Professor, Department of Computer Science and Engineering, National Institute of Technology
The students were actively participated in the webinar event and acquired knowledge on AI/ML basics, Drug discoveries, block chain in healthcare, healthcare data analytics, software sources available and internship opportunities. Some healthcare and technology innovators are collaborating and trying to change our current reality by experimenting with artificial intelligence (AI) and machine learning. Computers and the algorithms they run can scrub colossal amounts of data—much faster and more accurately than human scientists or medical professionals—to unearth patterns and predictions to enhance disease diagnosis, inform treatment plans and enhance public health and safety.
Based on the extraordinary impact improvements to the healthcare system can have for so many people and its potential to save lives and money, healthcare has become a key industry for investment and efforts for AI and machine learning. Not only have major players such as IBM and Microsoft jumped into their own AI healthcare projects, but several start-ups and smaller organisations have begun their own efforts to create tools to aid healthcare.
Artificial intelligence and machine learning in healthcare will continue to get better and impact disease prevention and diagnosis, extract more meaning from data across various clinical trials, help develop customized drugs based on an individual’s unique DNA and inform treatment options among other things

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