Unlocking the Secrets of AI: Advancements in Explainable Machine Learning for Healthcare
Artificial Intelligence (AI) and machine learning (ML) have revolutionized the healthcare industry with their potential to improve diagnostics and treatment outcomes. However, the lack of transparency and interpretability of AI algorithms has raised concerns among clinicians and patients. To address this issue, researchers are making significant advancements in explainable ML techniques to provide insights into the decision-making process of AI models.
In an upcoming special issue, the focus will be on exploring recent breakthroughs and techniques in explainable artificial intelligence (XAI) for healthcare applications. The objective is to gain the trust of clinicians and patients by providing explanations about the decisions made by ML and deep learning (DL) models.
The special issue welcomes research articles and reviews on various topics related to explainable ML techniques in healthcare. Some of the areas of interest include:
1. Explainable and interpretable ML techniques for healthcare applications
2. Advancements in XAI for clinical decision support systems
3. Explainability methods for deep learning models in medical imaging
4. Interpretable ML algorithms for predictive modeling in personalized medicine
5. Ethical considerations and social impact of explainable AI in healthcare
Researchers are encouraged to submit their manuscripts online through the MDPI website. All submissions will undergo a rigorous peer-review process before acceptance. Accepted papers will be published continuously in the journal and listed on the special issue website.
It is important to note that submitted manuscripts should be original and not published elsewhere. The article should adhere to the instructions for authors provided on the journal’s website.
Diagnostics, an international peer-reviewed open access journal published by MDPI, will be the platform for disseminating the research findings. Authors are required to pay an Article Processing Charge (APC) of 2600 CHF (Swiss Francs) for publication in this open access journal.
To ensure the quality and clarity of the manuscripts, authors can utilize MDPI’s English editing service before submission. This will help improve the formatting and language of the papers, making them more reader-friendly.
Explainable AI is an evolving field with significant implications for healthcare. By enhancing the transparency and interpretability of ML algorithms, clinicians and patients can have trust in the decisions made by AI models. This special issue aims to showcase the latest advancements in XAI techniques, paving the way for wider adoption and integration of AI in healthcare.
Please visit the MDPI website for more information on submission guidelines and to keep up with the latest updates on this special issue.
