Explainable AI for ICT: System and Software Architecture

Dave, Devam; Naik, Het; Singhal, Smiti; Dwivedi, Rudresh; Patel, Pankesh

doi:10.1007/978-981-19-2374-6_8

Cited by 5 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Implementing effective and profitable deep learning based healthcare models needs the cooperation of many different organizations and assets, as well as a variety of different technologies, algorithms, scripts, libraries, and tools for automating data preprocessing, digestion, training, validation, and production. To finish a challenging task, a workflow, or logically connected stream of several procedures, is needed [ 433 – 435 ].…”

Section: Workflowmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.

show abstract

Section: Workflowmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…Healthcare practitioners can now get real-time alerts and information about any deviations from the anticipated trajectory of patient care thanks to this feature. Wearables with AI capabilities, for instance, can track a patient's vital signs over time and identify minute variations that might portend the start of a possible adverse effect [31].…”

Section: Early Identification Of Potential Side Effectsmentioning

confidence: 99%

“…Sensitive information on a patient's medical history, diagnosis, and treatments is frequently included in healthcare data. AI applications need to put patient confidentiality first, making sure that private information is shielded from prying eyes [31].…”

Section: Challenges and Considerationsmentioning

confidence: 99%

Artificial Intelligence in Healthcare Delivery and Control Side Effects

Jawaid

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Existing methods for explaining machine learning model predictions have been extensively studied, such as in the studies by Pintelas et al [12], Tasin et al [13], Davagdorj et al [14], Abdulsalam et al [15], Gao et al [16], Joseph et al [17], Ibrahim et al [18], Du et al [19], Nagaraj et al [20], Maillot and Thonnat [21], Icarte et al [22], Daniels et al [23], Zafar and Khan [24], Srinivasu et al [25], Gerlings et al [26], and Dave et al [27]. However, these methods often have limitations in providing comprehensive and easily understandable insights into the decision-making process [28].…”

Section: Introductionmentioning

confidence: 99%

A Mobile App That Addresses Interpretability Challenges in Machine Learning–Based Diabetes Predictions: Survey-Based User Study

Hendawi,

Li,

Roy

2023

JMIR Form Res

View full text Add to dashboard Cite

Background Machine learning approaches, including deep learning, have demonstrated remarkable effectiveness in the diagnosis and prediction of diabetes. However, these approaches often operate as opaque black boxes, leaving health care providers in the dark about the reasoning behind predictions. This opacity poses a barrier to the widespread adoption of machine learning in diabetes and health care, leading to confusion and eroding trust. Objective This study aimed to address this critical issue by developing and evaluating an explainable artificial intelligence (AI) platform, XAI4Diabetes, designed to empower health care professionals with a clear understanding of AI-generated predictions and recommendations for diabetes care. XAI4Diabetes not only delivers diabetes risk predictions but also furnishes easily interpretable explanations for complex machine learning models and their outcomes. Methods XAI4Diabetes features a versatile multimodule explanation framework that leverages machine learning, knowledge graphs, and ontologies. The platform comprises the following four essential modules: (1) knowledge base, (2) knowledge matching, (3) prediction, and (4) interpretation. By harnessing AI techniques, XAI4Diabetes forecasts diabetes risk and provides valuable insights into the prediction process and outcomes. A structured, survey-based user study assessed the app’s usability and influence on participants’ comprehension of machine learning predictions in real-world patient scenarios. Results A prototype mobile app was meticulously developed and subjected to thorough usability studies and satisfaction surveys. The evaluation study findings underscore the substantial improvement in medical professionals’ comprehension of key aspects, including the (1) diabetes prediction process, (2) data sets used for model training, (3) data features used, and (4) relative significance of different features in prediction outcomes. Most participants reported heightened understanding of and trust in AI predictions following their use of XAI4Diabetes. The satisfaction survey results further revealed a high level of overall user satisfaction with the tool. Conclusions This study introduces XAI4Diabetes, a versatile multi-model explainable prediction platform tailored to diabetes care. By enabling transparent diabetes risk predictions and delivering interpretable insights, XAI4Diabetes empowers health care professionals to comprehend the AI-driven decision-making process, thereby fostering transparency and trust. These advancements hold the potential to mitigate biases and facilitate the broader integration of AI in diabetes care.

show abstract

Explainable AI for ICT: System and Software Architecture

Cited by 5 publications

References 6 publications

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Artificial Intelligence in Healthcare Delivery and Control Side Effects

A Mobile App That Addresses Interpretability Challenges in Machine Learning–Based Diabetes Predictions: Survey-Based User Study

Contact Info

Product

Resources

About