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A foundation model, also known as large X model (LxM), is a machine learning or deep learning model that is trained on vast datasets so it can be applied across a wide range of use cases. [1] Generative AI applications like Large Language Models are often examples of foundation models.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
Such applications outside the healthcare system raise various professional, ethical and regulatory questions. [106] Another issue is often with the validity and interpretability of the models. Small training datasets contain bias that is inherited by the models, and compromises the generalizability and stability of these models.
Easterling and colleagues (REF LHS 2022) proffer an elaborate taxonomy of LHS elements and use this to describe an LHS-IP, or “Learning Health System In Practice” as a model for health care systems who seek to become an LHS. [26] The motivations for applying LHS concepts are largely and logically focused on improving the quality of care.
Generative AI systems trained on words or word tokens include GPT-3, GPT-4, GPT-4o, LaMDA, LLaMA, BLOOM, Gemini and others (see List of large language models). They are capable of natural language processing, machine translation, and natural language generation and can be used as foundation models for other tasks. [62]
Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images, or video.This integration allows for a more holistic understanding of complex data, improving model performance in tasks like visual question answering, cross-modal retrieval, [1] text-to-image generation, [2] aesthetic ranking, [3] and ...
COVID-19 simulation models are mathematical infectious disease models for the spread of COVID-19. [1] The list should not be confused with COVID-19 apps used mainly for digital contact tracing. Note that some of the applications listed are website-only models or simulators, and some of those rely on (or use) real-time data from other sources.
AI systems also require constant monitoring to detect and mitigate vulnerabilities that may arise post-deployment. In high-stakes environments like autonomous systems and healthcare, engineers incorporate redundancy and fail-safe mechanisms to ensure that AI models continue to function correctly in the presence of security threats. [21]