Applications of continual learning machine learning in clinical practice

By Cecilia S. Lee, Aaron Y. Lee. 2020

213 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Informatics

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Citations in the corpus

Online Coreset Selection for Rehearsal-based Continual Learning (2021, neutral): “In light of this need, continual learning (CL) [40], or lifelong learning, tackles a learning scenario where a model continuously learns over a sequence of tasks [18, 25] within a broad research area, such as classification [15, 9], image generation [47], language learning [24, 4], clinical applicat”
Online Continual Learning in Image Classification: An Empirical Survey (2021, background): “[152] discusses the clinical application of CL from three perspectives: diagnosis, prediction and treatment decisions.”
Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension (2020, unknown): “The group noted that, while of interest, this field is relatively early in its development without tangible examples in healthcare applications, and that it would not be appropriate for it to be addressed by SPIRIT-AI at this stage 49 .”
Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare (2022, background): “Nevertheless, there are many challenges in implementing continual updating procedures 13 .”
Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension (2020, background): “60 This topic will be monitored and will be revisited in future iterations of CONSORT-AI.”
A Continual Learning Survey: Defying Forgetting in Classification Tasks (2019, background): “Albeit a challenging problem, progress in continual learning has led to real-world applications starting to emerge [21], [22], [23]. ﬁguration, with an exclusive output layer or head for each task.”
AI in health and medicine (2022, background): “Specific regulatory challenges arise from continual learning, where models learn from new data over time and adjust to shifts in patient populations, as this may come at the risk of overwriting previously learned patterns or otherwise causing new mistakes 106 .”
The myth of generalisability in clinical research and machine learning in health care (2020, methodology): “29 This path to validation will probably require more work than simply evaluating a machine learning system on multiple datasets and then claiming universal external generalisability, as vendors might wish to do.”