Machine Learning - Transforming Healthcare, Episode 8 Part 1
Timestops (8)
Tools Used
Topic Overview
Key Takeaways
- Machine learning is a subset of AI that learns from past data without explicit programming, unlike AI which interacts with environments.
- ML has two core purposes: classifying data using trained models and predicting future outcomes based on patterns.
- ML excels at speed and scale beyond human capability but requires massive computational power for pattern recognition tasks.
- Clinical example: ML can classify cancerous moles using computer vision and supervised learning on dermatology datasets.
- Understanding ML limitations is critical—it automates specific tasks but cannot replicate general human reasoning.
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Transcript
Hey everyone, we are back with a new episode of our Transforming Healthcare series, where we introduce technologies that we believe will transform healthcare. In one of our previous episodes, we explained artificial intelligence, and today we're talking about machine learning. If you watched our previous videos, you already know that artificial intelligence is a technology that permits the machine to simulate human behavior. And machine learning is a soft field of AI that allows a machine to automatically learn from past data without programming. The difference between machine learning and AI is frequently misunderstood. Machine learning learns and predicts based on passive observation, whereas artificial intelligence implies an agent interacting with the environment to learn and take actions that maximize its chance of successfully achieving its goals. Machine learning algorithms allow us to use the data we gather to make predictions about the future, taking humans out of the equation as much as possible. Present day machine learning has two purposes. One is, classify data based on the models which have been developed. The other one is, making predictions about future outcomes. The hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify cancerous moles. Algorithm for stock trading may predict the future potential trends. Machine learning has proven valuable because it can solve problems at a speed and scale that can't be duplicated by the human mind alone. With massive computational ability for a single task or multiple specific tasks, machines can be trained to identify patterns in and relationships between input data and automate routine processes. But it's important to understand what machine learning can and cannot do. Stay tuned for our next video where we'll focus on applications of machine learning in healthcare. Download the StayQured app, follow us on social media, and subscribe on YouTube channel. And remember, knowledge should be free.