AI in Healthcare -Closer Look!
In this article, I will be taking you through some of the practical considerations and challenges of using AI in Medical diagnosis. The criticality involved in this domain makes it important to pay heed to these challenges apart from the core AI/ML practices.
Dataset considerations
- Imbalanced dataset —The presence of a higher number of data samples without disease rather than with disease results in imbalanced datasets and thus poses issues in training algorithms for medical analysis.
Solution:
a) Resampling data (Oversampling, Undersampling, etc.)
b) Modifying loss function i.e. using weighted loss to incorporate the effect of imbalanced classes.
2. Small training dataset — Absence of labeled dataset or small training data availability especially for Image analysis tasks like thoracic disease detection using Chest-Xray, Brain Tumor detection using MRI Scans, etc.
Solution:
a) Transfer Learning- Transfer learning implies adapting a network trained for one problem to a different problem. With transfer learning, we can build good classifiers with a few hundred images. Find more info on transfer learning here.
b) Data Augmentation- We can use different transformations on the data to increase the breadth of training data. The only thing we need to be careful in the case of medical image data transformations is to choose transformations keeping in mind that post-transformation Y-label(Ground truth) remains true.
For eg. If on a chest X-ray, a horizontal flip is used that will cause the heart to appear on a different side implies a separate kind of medical problem. Find more info on data augmentation here.
Model Testing considerations
- Patient overlap- While creating training, validation and test set it should be made sure that there is no patient overlap among these sets as this issue can result in over-optimistic test results due to the same patients present in training and test sets. Splits should happen based on patients to resolve this issue.
- Deciding ground truth- To decide on ground truth for a sample is a different challenge that may arise due to interobserver disagreement where one practitioner's opinion may differ from another and is quite common in the medicine domain. Consensus voting where a group of human experts is considered to determine the ground truth can be a way to tackle this issue.
Practical Considerations
- Different populations’ characteristics— Achieving reliable generalization is a challenge for AI models using medical data. For e.g. Chest X-ray data for India may look quite different from the US so a model built on US patient’s data may not be very effective for other geographies.
- External validation — While the AI models are built on historical data but how it works on real-world data marks whether the AI model can be directly used or it needs to be fine-tuned as per new data. However, to understand the utility of AI models in the real world, these need to be applied to real-world data(prospective data).
So, we have reached the end of this post and learned about dataset challenges, model testing, and practical implementation challenges that can be examined for healthcare use cases.
References :
- https://www.coursera.org/learn/ai-for-medical-diagnosis/ungradedLab/gjDcc/counting-labels-and-weighted-loss-function
- https://www.coursera.org/learn/ai-for-medical-diagnosis/lecture/VNkO2/multi-task-loss-dataset-size-and-cnn-architectures
- https://www.coursera.org/learn/ai-for-medical-diagnosis/lecture/RVBZK/measuring-patient-outcomes
