Explainable AI for clinical and remote health applications: a survey on
tabular and time series data
- URL: http://arxiv.org/abs/2209.06528v1
- Date: Wed, 14 Sep 2022 10:01:29 GMT
- Title: Explainable AI for clinical and remote health applications: a survey on
tabular and time series data
- Authors: Flavio Di Martino, Franca Delmastro
- Abstract summary: It is worth noting that XAI has not gathered the same attention across different research areas and data types, especially in healthcare.
This paper provides a review of the literature in the last 5 years, illustrating the type of generated explanations and the efforts provided to evaluate their relevance and quality.
- Score: 3.655021726150368
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Nowadays Artificial Intelligence (AI) has become a fundamental component of
healthcare applications, both clinical and remote, but the best performing AI
systems are often too complex to be self-explaining. Explainable AI (XAI)
techniques are defined to unveil the reasoning behind the system's predictions
and decisions, and they become even more critical when dealing with sensitive
and personal health data. It is worth noting that XAI has not gathered the same
attention across different research areas and data types, especially in
healthcare. In particular, many clinical and remote health applications are
based on tabular and time series data, respectively, and XAI is not commonly
analysed on these data types, while computer vision and Natural Language
Processing (NLP) are the reference applications. To provide an overview of XAI
methods that are most suitable for tabular and time series data in the
healthcare domain, this paper provides a review of the literature in the last 5
years, illustrating the type of generated explanations and the efforts provided
to evaluate their relevance and quality. Specifically, we identify clinical
validation, consistency assessment, objective and standardised quality
evaluation, and human-centered quality assessment as key features to ensure
effective explanations for the end users. Finally, we highlight the main
research challenges in the field as well as the limitations of existing XAI
methods.
Related papers
- TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [57.067409211231244]
This paper presents meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design.
We provide basic validation methods for each task to ensure the datasets' usability and reliability.
We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
arXiv Detail & Related papers (2024-06-30T09:13:10Z) - 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) - 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) - Explainable AI for Malnutrition Risk Prediction from m-Health and
Clinical Data [3.093890460224435]
This paper presents a novel AI framework for early and explainable malnutrition risk detection based on heterogeneous m-health data.
We performed an extensive model evaluation including both subject-independent and personalised predictions.
We also investigated several benchmark XAI methods to extract global model explanations.
arXiv Detail & Related papers (2023-05-31T08:07:35Z) - A Brief Review of Explainable Artificial Intelligence in Healthcare [7.844015105790313]
XAI refers to the techniques and methods for building AI applications.
Model explainability and interpretability are vital successful deployment of AI models in healthcare practices.
arXiv Detail & Related papers (2023-04-04T05:41:57Z) - Informing clinical assessment by contextualizing post-hoc explanations
of risk prediction models in type-2 diabetes [50.8044927215346]
We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state.
We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability.
Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
arXiv Detail & Related papers (2023-02-11T18:07:11Z) - Benchmark datasets driving artificial intelligence development fail to
capture the needs of medical professionals [4.799783526620609]
We released a catalogue of datasets and benchmarks pertaining to the broad domain of clinical and biomedical natural language processing (NLP)
A total of 450 NLP datasets were manually systematized and annotated with rich metadata.
Our analysis indicates that AI benchmarks of direct clinical relevance are scarce and fail to cover most work activities that clinicians want to see addressed.
arXiv Detail & Related papers (2022-01-18T15:05:28Z) - CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark [51.38557174322772]
We present the first Chinese Biomedical Language Understanding Evaluation benchmark.
It is a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification.
We report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling.
arXiv Detail & Related papers (2021-06-15T12:25:30Z) - The Medkit-Learn(ing) Environment: Medical Decision Modelling through
Simulation [81.72197368690031]
We present a new benchmarking suite designed specifically for medical sequential decision making.
The Medkit-Learn(ing) Environment is a publicly available Python package providing simple and easy access to high-fidelity synthetic medical data.
arXiv Detail & Related papers (2021-06-08T10:38:09Z) - Detecting Spurious Correlations with Sanity Tests for Artificial
Intelligence Guided Radiology Systems [22.249702822013045]
A critical component to deploying AI in radiology is to gain confidence in a developed system's efficacy and safety.
The current gold standard approach is to conduct an analytical validation of performance on a generalization dataset.
We describe a series of sanity tests to identify when a system performs well on development data for the wrong reasons.
arXiv Detail & Related papers (2021-03-04T14:14:05Z) - Survey of XAI in digital pathology [3.4591414173342643]
We present a survey on XAI within digital pathology, a medical imaging sub-discipline with particular characteristics and needs.
We give a thorough overview of current XAI techniques of potential relevance for deep learning methods in pathology imaging.
In doing, we incorporate uncertainty estimation methods as an integral part of the XAI landscape.
arXiv Detail & Related papers (2020-08-14T13:11:54Z)
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.