Artificial Intelligence, Machine Learning, And Natural Language Processing Tools For Generating Multimodal (Visual, Textual, And Audio) Intensive Care Unit Journey And Discharge Summaries: A Scoping Review - Info and Reading Options
By Gideon Johnson
“Artificial Intelligence, Machine Learning, And Natural Language Processing Tools For Generating Multimodal (Visual, Textual, And Audio) Intensive Care Unit Journey And Discharge Summaries: A Scoping Review” Metadata:
- Title: ➤ Artificial Intelligence, Machine Learning, And Natural Language Processing Tools For Generating Multimodal (Visual, Textual, And Audio) Intensive Care Unit Journey And Discharge Summaries: A Scoping Review
- Author: Gideon Johnson
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- Internet Archive ID: osf-registrations-9nyf6-v1
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"Artificial Intelligence, Machine Learning, And Natural Language Processing Tools For Generating Multimodal (Visual, Textual, And Audio) Intensive Care Unit Journey And Discharge Summaries: A Scoping Review" Description:
The Internet Archive:
This scoping review aims to systematically map the existing literature on artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) tools developed to generate multimodal (visual, textual, and audio) summaries of the intensive care unit (ICU) journey and discharge experience. Survivors of critical illness often experience fragmented transitions of care due to inadequate communication about their ICU stay. Traditional discharge summaries tend to be clinician-centred and predominantly text-based, often excluding contextual and patient-understandable information. In contrast, emerging AI-driven technologies offer the potential to automatically generate personalised summaries that combine narrative text, visual timelines, and audio explanations, improving continuity of care and patient understanding. Despite the rapid advancement of AI applications in healthcare, there is currently no comprehensive review examining how such multimodal summary tools have been applied or developed specifically for the ICU context. This review will identify and describe existing tools, their output modalities (textual, visual, audio), target users (clinicians, patients, families), and reported outcomes, limitations, and implementation factors. The findings will inform the development of future AI-based solutions for ICU survivorship and recovery communication. This review also forms a key evidence base for the Critical Illness Brain Dysfunction Survivorship Plus (CIBS+) programme and supports ongoing NIHR and Wellcome-funded research into personalised post-ICU digital health tools.
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"Artificial Intelligence, Machine Learning, And Natural Language Processing Tools For Generating Multimodal (Visual, Textual, And Audio) Intensive Care Unit Journey And Discharge Summaries: A Scoping Review" is available for download from The Internet Archive in "data" format, the size of the file-s is: 0.12 Mbs, and the file-s went public at Sun Aug 10 2025.
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- Number of Available Files: 5
- Added Date: 2025-08-10 13:00:23
- Scanner: Internet Archive Python library 1.9.9
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