AI in Healthcare- Intro to ML

According to the session, how does machine learning differ from traditional programming in healthcare applications?

• It relies on explicit rules coded by humans.

• It learns patterns from data examples instead of rules.

• It only processes structured data like spreadsheets.

• It requires no data preprocessing.

Which of the following best describes the relationship between AI, ML, and Deep Learning as outlined in the introduction?

• AI is a subset of ML, and Deep Learning is separate from both

• Deep Learning is the broadest category, encompassing AI and ML.

• All three are independent fields with no overlap.

• ML is a subset of AI, and Deep Learning is a Extension of ML using neural networks.

In the context of why ML is used in healthcare for patients, what is one key benefit mentioned for emergencies?

• Reducing General administrative paperwork.

• Scheduling appointments more efficiently.

• Faster diagnosis to save lives.

• Automating patient billing.

Which type of machine learning is described as finding hidden groups in data without labels?

• Unsupervised Learning.

• Supervised Learning.

• Deep Learning.

• Reinforcement Learning.

Among the healthcare applications of ML listed, which one involves analyzing retinal images for diabetic retinopathy?

• Diagnostics.

• Drug Discovery.

• Wearables.

• Telemedicine.Telemedicine.

What is the third step in the ML workflow in healthcare after collecting data which enhance the quality of data?

• Train the Model.

• Deploy & Monitor.

• Preprocess Data (e.g., clean missing values, normalize)

• Define the Problem.

Which challenge in healthcare ML refers to models potentially favoring certain groups due to imbalanced datasets?

• Privacy concerns.

• Bias in datasets.

• Data quality.

• Interpretability.

In the case study on predicting diabetes risk, what type of ML method was used, and what was the model's output?

• Unsupervised Learning; Groups of patients.

• Supervised Learning (classification); "Yes/No."

• Deep Learning; Image analysis

• Reinforcement Learning; Rewards for predictions.

Scenario: A hospital uses an AI system to group patients with similar symptoms without any pre-labeled data, helping to identify new patterns in disease outbreaks. What type of machine learning is this?

• Supervised Learning.

• Unsupervised Learning.

• Reinforcement Learning.

• Manual Learning.

Scenario: A doctor reviews patient history data labeled as "high risk" or "low risk" for heart disease and trains a model to predict future cases. What type of machine learning is being applied here?

• Unsupervised Learning.

• Reinforcement Learning.

• Supervised Learning.

• No learning involved.