Description
The Pulse of Healthcare AI is a thought-provoking webinar that explores how artificial intelligence is reshaping modern healthcare delivery. The session will highlight real-world applications of AI across diagnostics, clinical decision-making, imaging, and workflow optimization. Experts will discuss current trends, practical use cases, and key challenges in adopting AI responsibly in clinical practice. Designed for healthcare professionals, the webinar aims to bridge the gap between innovation and everyday clinical impact, offering actionable insights for evidence-based, AI-enabled care.
Summary Listen
- The speaker begins by thanking the diversity team and acknowledging the rapid advancements in technology, particularly in healthcare, where AI applications are becoming increasingly prevalent. They introduce their roles as a trainer at Premium Solutions, an AI consultant at the Navy B's USA, and a faculty member at Savita Jandu College, along with their involvement in a startup at IIT Shending focused on manufacturing products from their patent.
- The presentation outlines the history of AI, starting from the 17th century with the invention of the spinning machine and progressing through Alan Turing's machine to the early visions of AI in the mid-20th century. It also discusses the AI winter period, where funding did not yield productive results due to lack of software and skilled professionals, leading to the modern AI boom. The classification of AI is simplified into machine learning, generative tools, computer vision, deep learning, and expert systems.
- The speaker discusses potential negative perceptions and impacts of AI, mentioning the need for clarity on its efficient usage and how to prevent it from becoming detrimental. He then categorizes different data types into text, image, video, and audio. They discuss that AI is not meant to replace doctors but augment them, improving accuracy and efficiency.
- The "QCQA" concept (Quantity, Cost, Quality, Adoption) is introduced as a crucial element to consider when proposing new innovations. The presentation explores where AI can be used in healthcare, categorized by age groups and including prevention, diagnosis and classification, treatment planning, surgical execution, recovery and rehabilitation, and post-surveillance. He uses current and future examples to explain what is happening and what might happen in each aspect.
- The presentation touches upon various current AI applications in healthcare, such as smart tracking for mental health, cancer screening, smart watches for vital sign monitoring, diagnosis of eye diseases, and various cancer diagnosis methods. It also delves into the potential future of AI in healthcare, including multimodal diagnostic solutions, real-time pathogen identification, voice-based neurological disorder detection, and breath analysis for lung cancer.
- The speaker uses a video to showcase their surgical planning process and discusses the potential for AI to generate surgical protocols, create patient-specific simulation models, and predict complications. It discusses about robotic surgeries and a video shows a surgeon doing a microsurgery on a corn kernel and there is another video for navigation surgery. It explores the potential for autonomous surgical procedures, haptic feedback enhancement, real-time decision-making, and the use of swarm robotics or nanobots.
- The presentation covers the use of AI in rehabilitation and post-surveillance, including ICU monitoring systems, medication management systems, recovery tracking, and personalized rehab programs. It delves into pharmacogenomics, cancer target treatments, biomarker-driven drug selection, digital twins, lifestyle genomic intervention, and preventive precision interventions. It also touches upon the technology for individuals with neurological abnormalities.
- Finally, the speaker discusses the reasons why AI is needed in healthcare, including global physician shortages, rising healthcare costs, increasing disease complexity, aging populations, and medical errors and diagnostic delays. They outline the limitations of AI, such as data quality and quantity, ethical concerns, clinical and demographic limitations, authenticity and validation, complex mechanisms, expenses, and the need for a multidisciplinary approach.
Sample Certificate
About the Speakers
Dr. Thirumurugan S. V. M.
AI Healthcare Consultant, Labelbees, Adjunct Faculty, Saveetha Medical College and Hospital, Tamil Nadu
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