The Role of Explainable AI in Revolutionizing Human Health Monitoring
- URL: http://arxiv.org/abs/2409.07347v2
- Date: Fri, 13 Sep 2024 14:32:10 GMT
- Title: The Role of Explainable AI in Revolutionizing Human Health Monitoring
- Authors: Abdullah Alharthi, Ahmed Alqurashi, Turki Alharbi, Mohammed Alammar, Nasser Aldosari, Houssem Bouchekara, Yusuf Shaaban, Mohammad Shoaib Shahriar, Abdulrahman Al Ayidh,
- Abstract summary: Explainable AI (XAI) offers greater clarity and has the potential to significantly improve patient care.
This literature review focuses on chronic conditions such as Parkinson's, stroke, depression, cancer, heart disease, and Alzheimer's disease.
The article is concluded with a critical appraisal of the challenges and future research opportunities for XAI in human health monitoring.
- Score: 0.0
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: The complex nature of disease mechanisms and the variability of patient symptoms present significant obstacles in developing effective diagnostic tools. Although machine learning has made considerable advances in medical diagnosis, its decision-making processes frequently lack transparency, which can jeopardize patient outcomes. This underscores the critical need for Explainable AI (XAI), which not only offers greater clarity but also has the potential to significantly improve patient care. In this literature review, we conduct a detailed analysis of analyzing XAI methods identified through searches across various databases, focusing on chronic conditions such as Parkinson's, stroke, depression, cancer, heart disease, and Alzheimer's disease. The literature search revealed the application of 9 trending XAI algorithms in the field of healthcare and highlighted the pros and cons of each of them. Thus, the article is concluded with a critical appraisal of the challenges and future research opportunities for XAI in human health monitoring.
Related papers
- MAGDA: Multi-agent guideline-driven diagnostic assistance [43.15066219293877]
In emergency departments, rural hospitals, or clinics in less developed regions, clinicians often lack fast image analysis by trained radiologists.
In this work, we introduce a new approach for zero-shot guideline-driven decision support.
We model a system of multiple LLM agents augmented with a contrastive vision-language model that collaborate to reach a patient diagnosis.
arXiv Detail & Related papers (2024-09-10T09:10:30Z) - Breast Cancer Diagnosis: A Comprehensive Exploration of Explainable Artificial Intelligence (XAI) Techniques [38.321248253111776]
Article explores the application of Explainable Artificial Intelligence (XAI) techniques in the detection and diagnosis of breast cancer.
Aims to highlight the potential of XAI in bridging the gap between complex AI models and practical healthcare applications.
arXiv Detail & Related papers (2024-06-01T18:50:03Z) - A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring.
As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs.
The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
arXiv Detail & Related papers (2024-05-21T06:44:40Z) - Emotional Intelligence Through Artificial Intelligence : NLP and Deep Learning in the Analysis of Healthcare Texts [1.9374282535132377]
This manuscript presents a methodical examination of the utilization of Artificial Intelligence in the assessment of emotions in texts related to healthcare.
We scrutinize numerous research studies that employ AI to augment sentiment analysis, categorize emotions, and forecast patient outcomes.
There persist challenges, which encompass ensuring the ethical application of AI, safeguarding patient confidentiality, and addressing potential biases in algorithmic procedures.
arXiv Detail & Related papers (2024-03-14T15:58:13Z) - The Significance of Machine Learning in Clinical Disease Diagnosis: A
Review [0.0]
This research investigates the capacity of machine learning algorithms to improve the transmission of heart rate data in time series healthcare metrics.
The factors under consideration include the algorithm utilized, the types of diseases targeted, the data types employed, the applications, and the evaluation metrics.
arXiv Detail & Related papers (2023-10-25T20:28:22Z) - Rethinking Human-AI Collaboration in Complex Medical Decision Making: A
Case Study in Sepsis Diagnosis [34.19436164837297]
We build SepsisLab based on a state-of-the-art AI algorithm and extend it to predict the future projection of sepsis development.
We demonstrate that SepsisLab enables a promising human-AI collaboration paradigm for the future of AI-assisted sepsis diagnosis.
arXiv Detail & Related papers (2023-09-17T19:19:39Z) - Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges [58.32937972322058]
"Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image (MedAI 2021)" competitions.
We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic.
arXiv Detail & Related papers (2023-07-30T16:08:45Z) - XAI Renaissance: Redefining Interpretability in Medical Diagnostic
Models [0.0]
The XAI Renaissance aims to redefine the interpretability of medical diagnostic models.
XAI techniques empower healthcare professionals to understand, trust, and effectively utilize these models for accurate and reliable medical diagnoses.
arXiv Detail & Related papers (2023-06-02T16:42:20Z) - Explainable Deep Learning in Healthcare: A Methodological Survey from an
Attribution View [36.025217954247125]
We introduce the methods for interpretability in depth and comprehensively as a methodological reference for future researchers or clinical practitioners.
We discuss how these methods have been adapted and applied to healthcare problems and how they can help physicians better understand these data-driven technologies.
arXiv Detail & Related papers (2021-12-05T17:12:53Z) - Achievements and Challenges in Explaining Deep Learning based
Computer-Aided Diagnosis Systems [4.9449660544238085]
We discuss early achievements in development of explainable AI for validation of known disease criteria.
We highlight some of the remaining challenges that stand in the way of practical applications of AI as a clinical decision support tool.
arXiv Detail & Related papers (2020-11-26T08:08:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.