Shifting machine learning for healthcare from development to deployment and from models to data

Area of Science:

• Healthcare Technology
• Artificial Intelligence
• Data Science

Background:

• Machine learning (ML) has significantly advanced healthcare over the last decade, automating tasks and improving clinical capabilities.
• The central role of data in ML applications, from initial development to final deployment, is increasingly recognized.

Purpose of the Study:

• To provide a data-centric perspective on the innovations and challenges in healthcare ML.
• To review strategies for dataset augmentation and advanced modeling techniques for clinical text.

Main Methods:

• Discussion of deep generative models and federated learning for dataset augmentation.
• Exploration of transformer models for handling large datasets and clinical text analysis.
• Analysis of data-related challenges in ML deployment, including data delivery and handling data shifts.

Main Results:

• Deep generative models and federated learning offer methods to enhance ML model performance by augmenting datasets.
• Transformer models show promise in managing larger datasets and improving the analysis of clinical text.
• Efficient data delivery and adaptation to data shifts are critical for successful ML deployment in clinical settings.

Conclusions:

• Data is fundamental to the progress and successful deployment of ML in healthcare.
• Advanced ML techniques and careful data management are essential for realizing the full potential of AI in medicine.