Updating methods for artificial intelligence-based clinical prediction models: a scoping review

Abstract

Objectives: To give an overview of methods for updating artificial intelligence (AI)-based clinical prediction models based on new data.

Study design and setting: We comprehensively searched Scopus and Embase up to August 2022 for articles that addressed developments, descriptions, or evaluations of prediction model updating methods. We specifically focused on articles in the medical domain involving AI-based prediction models that were updated based on new data, excluding regression-based updating methods as these have been extensively discussed elsewhere. We categorized and described the identified methods used to update the AI-based prediction model as well as the use cases in which they were used.

Results: We included 78 articles. The majority of the included articles discussed updating for neural network methods (93.6%) with medical images as input data (65.4%). In many articles (51.3%) existing, pretrained models for broad tasks were updated to perform specialized clinical tasks. Other common reasons for model updating were to address changes in the data over time and cross-center differences; however, more unique use cases were also identified, such as updating a model from a broad population to a specific individual. We categorized the identified model updating methods into four categories: neural network-specific methods (described in 92.3% of the articles), ensemble-specific methods (2.5%), model-agnostic methods (9.0%), and other (1.3%). Variations of neural network-specific methods are further categorized based on the following: (1) the part of the original neural network that is kept, (2) whether and how the original neural network is extended with new parameters, and (3) to what extent the original neural network parameters are adjusted to the new data. The most frequently occurring method (n = 30) involved selecting the first layer(s) of an existing neural network, appending new, randomly initialized layers, and then optimizing the entire neural network.

Conclusion: We identified many ways to adjust or update AI-based prediction models based on new data, within a large variety of use cases. Updating methods for AI-based prediction models other than neural networks (eg, random forest) appear to be underexplored in clinical prediction research.

Plain language summary: AI-based prediction models are increasingly used in health care, helping clinicians with diagnosing diseases, guiding treatment decisions, and informing patients. However, these prediction models do not always work well when applied to hospitals, patient populations, or times different from those used to develop the models. Developing new models for every situation is neither practical nor desired, as it wastes resources, time, and existing knowledge. A more efficient approach is to adjust existing models to new contexts ('updating'), but there is limited guidance on how to do this for AI-based clinical prediction models. To address this, we reviewed 78 studies in detail to understand how researchers are currently updating AI-based clinical prediction models, and the types of situations in which these updating methods are used. Our findings provide a comprehensive overview of the available methods to update existing models. This is intended to serve as guidance and inspiration for researchers. Ultimately, this can lead to better reuse of existing models and improve the quality and efficiency of AI-based prediction models in health care.

Keywords: Artificial intelligence; Knowledge transfer; Machine learning; Model updating; Prediction models; Transfer learning.