Explainable, trustworthy, and ethical machine learning for healthcare: A survey
<p dir="ltr">With the advent of machine learning (ML) and deep learning (DL) empowered applications for critical applications like healthcare, the questions about liability, trust, and interpretability of their outputs are raising. The black-box nature of various DL models is a roadb...
محفوظ في:
| المؤلف الرئيسي: | Khansa Rasheed (17380573) (author) |
|---|---|
| مؤلفون آخرون: | Adnan Qayyum (16875936) (author), Mohammed Ghaly (17380576) (author), Ala Al-Fuqaha (4434340) (author), Adeel Razi (17380579) (author), Junaid Qadir (16494902) (author) |
| منشور في: |
2022
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
مواد مشابهة
-
Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study
حسب: Muhammad Atif Butt (10849980)
منشور في: (2023) -
A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI
حسب: Oishi Jyoti (21593819)
منشور في: (2025) -
Food fraud detection using explainable artificial intelligence
حسب: Okan Buyuktepe (17991493)
منشور في: (2023) -
Explainable Supervised and Semi-Supervised Learning for Breast Cancer Risk Prediction from Questionnaires: A Study on BCSC and UAE Datasets
حسب: Alsarookh, Omar Ahmad
منشور في: (2025) -
Secure and Robust Machine Learning for Healthcare: A Survey
حسب: Adnan Qayyum (16875936)
منشور في: (2020)