Explainable AI for Healthcare Informatics: Data or Algorithm/Model?
A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Health Informatics and Big Data".
Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 434
Special Issue Editor
Interests: AI; machine learning; computer vision; pattern recognition; biomedical imaging; healthcare informatics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Typical explainable AI aims at describing an AI model, its expected impact, and potential biases. However, the question is what if we have used data samples (e.g., numbers, texts, and images) that are not well interpreted and/or explained. In such a case, models may not turn in enough explainability except decision-making: binary or multi-class. In healthcare informatics, AI models expect to use/employ data that preserves respective clinical significance. This statement holds true for data augmentation in healthcare – do we have augmented data with clinical significance? In other words, augmented data cannot provide clinical manifestations due to specific diseaseX. As a result, explainable AI should not be limited to their AI algorithm but also their data.
In this special issue, authors are encouraged to submit their research articles (case study, original research article, and review/survey) with an aim of explainable AI – data or model.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:
Explainable AI
- Machine learning models: shallow, deep learning, and active learning
- Big data analytics and data visualization
- Applications: clinical text analytics, biomedical imaging, healthcare informatics and engineering, precision medicine, IOT, blockchain, and many more.
I look forward to receiving your contributions.
Prof. Dr. KC Santosh
Guest Editor
Manuscript Submission Information
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Keywords
- explainable AI
- big data analytics and data visualization
- speech/audio analysis
- precision medicine
- healthcare informatics
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