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Explainable AI: Methods, Frameworks, andTools forHealthcare 5.0
https://t.me/med1917
Additionally, it embraces virtual and augmented reality for training, surgical procedures, and patient education. Healthcare 4.0 emphasizes personalized and precision
medicine, utilizing genomics and biomarkers to deliver targeted therapies. It also
promotes remote patient monitoring, telemedicine, and smart healthcare devices,
enhancing accessibility, efciency, and patient engagement [7].
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1.5 Healthcare 5.0
Healthcare 5.0 represents a visionary future of healthcare that combines the power
of advanced technology with a human-centric approach. It acknowledges the importance of empathy, compassion, and the human touch in healthcare delivery.
Healthcare 5.0 envisions a harmonious integration of technological advancements,
such as AI and robotics, with the essential elements of human interaction and care.
Further, it focuses on fostering a strong patient-provider relationship, personalized
care experiences, and shared decision-making. Healthcare 5.0 prioritizes wellness,
preventive care, and mental health, considering the physical, emotional, and social
aspects of individuals. It embraces a holistic approach to health, to improve the
entire fullment and sophistication of life.
Healthcare 5.0, the eld of medical science and technology envisions a seamless
integration of numerous IoMT sensors which are communicated with network
infrastructure and enable data exchange that revolutionizes virtual health, intelligent
healthcare, and improves healthcare measures. Further, the technology combinations enable intelligent thin mobile devices to be seamlessly integrated with medical
sensors that deliver remote healthcare services. Conversely, improvement in IoMT
devices which are connected to patients, and provide dynamic medical data collection, progress monitoring, and health diagnoses condition [8, 9]. This data can be
transmitted to doctors and medical institutions without the need for extensive human
interaction.
Although, contemporary AI techniques have demonstrated their capabilities in
predicting future illnesses using genomic data [10], diagnosing critical diseases
through analysis of electronic health records (EHR) [11], and providing early recognition of critical illnesses with AI-based Early Warning Scores (EWS) [12], and
offering intelligent clinical decision support [13]. However, healthcare information
is extensive and varied, encompassing structured and unstructured forms like text,
imaging, and signal data. The healthcare unstructured data includes diagnosis
reports, clinical documents, digital health records, ward records, personal health
records, data from healthcare devices, wearable’s, and sensor data from smart
healthcare environments. However, the AI techniques in the healthcare paradigm
rises various challenges such as (i) lack of clarity and explainability in deployed AI
models and (ii) the biased predictive models can lead to misleading outcomes [14].
Therefore, to address these challenges, transparency is being emphasized in conventional AI design models [15]. However, current healthcare models are limited in
their ability to cope with pragmatic complexity and uncertainties [16]. Therefore,
Healthcare 5.0 requires the integration of Explainable Articial Intelligence (XAI)
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with intelligent healthcare and machines that enable robust, precise prediction and
transparency [16].
Accordingly, the chapter structure is described as Sect. 2 presents the compre-
hensive state of the art on XAI schemes and Healthcare 5.0 applications. Further
provides the comparative summary based on the XAI features. Section 3 describes
the numerous languages and libraries or toolkits leveraged in the implementation of
XAI-based healthcare models. Sections 4 and 5 present the future study prospects
and conclusion.
S. Pulipeti et al.
2 State oftheArt
Analytics are essential in providing solutions that are tailored to the needs of the
patient in the Healthcare 5.0 paradigm. For the XAI-based prediction for Healthcare
5.0, rst, the AI model performs the prediction according to input data and produces
the output as a prediction gain which is named a black box. Second, the interpretability of ML/DL models is validated by how it works and enables the stakeholders
to understand the decision of the information, this process named as white box [17].
The white box permits the transparency of analytics. In healthcare, future forecasts
are based on the patient’s present health status and records that necessitates the
development of accessible explanations for sophisticated trained models [18]. After
that, the XAI performance is assessed through XAI evaluation metrics which determine the quantitative and qualitative results that ensure transparency and trust
among the patient and doctors/medical practitioners for their actual practice. The
XAI is applied in various applications of healthcare including COVID-19 identication and prevention, cardiovascular disease, health monitoring, diagnosis, and many
more. The section presents the various XAI-based schemes employed in Healthcare
5.0, and comprehensive details are as follows.
Zhang etal. [19] present a survey on XAI methods leveraged in diagnosis and
surgery. The article discusses various XAI-enabled schemes and emphasizes the
challenges and future research directions. Further provides case study-based experimental results for breast cancer prediction and diagnosis with their accuracy.
However, the performance of the proposed scheme is evaluated using XAI metrics
but the in-depth analysis is essential from the medical experts’ perspective rather
than the data science expert’s point of view.
Yang et al. [20] proposed an XAI scheme for classifying COVID-19 patients
through CT images and identifying hydrocephalus patients using CT and MRI
datasets. The existing ML-based classication techniques collect the data from
numerous hospitals and perform the classication which degrades the performance because the acquired images are from different hospitals. The images are
visually different but collected from the same origin. Further, image annotations
are available patient level but not the image level, which requires a huge amount
of time to annotate it. Therefore, Zhang leveraged multicenter data collection from
COVID-19 patients’ CT images. The classication is applied using weakly supervised learning. Further, multi-model training is leveraged to optimize the gap
Explainable AI: Methods, Frameworks, andTools forHealthcare 5.0
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between thin and thick image slices. Then, the performance is analyzed, and generated quantitative and qualitative results. However, this model suffers from high
complexity and enhancement required to boost the efciency by employing optimization techniques.
Moradi etal. [21] explore existing AI techniques such as ML, DL, and Natural
Language Processing (NLP) in medicine. Further, expand the survey to emphasize
the signicance of XAI in future biomedical applications. However, this article provides a theoretical survey that uses images, continuous signals, and tabular data but
in the biomedical eld, the reports available in text format and how to process them
through XAI techniques are not covered.
Kwong etal. [22] create an explainable machine that predicts patients eligible for
nerve sparing, facilitating safer surgical preparation and counseling of patients. In
addition, clinicopathological specimens are collected from 900 lobes and then modeled using logistic regression (LR) and veried against existing datasets. The performance measures are computed and compared with the existing scheme. However,
this scheme does not incorporate the features of magnetic resonance imaging (MRI)
and provides poor sensitivity.
Fuhrman etal. [23] review examines various aspects of interpretable and XAI in
the context of evaluating COVID-19 disease. It emphasizes the importance of
restoring trust in AI applications for COVID-19 disease. The review covers the
identication of pertinent tasks of medical images in XAI, provides an outline of
contemporary approaches for generating explainable output in different imaging
scenarios, discusses the evaluation of XAI, and suggests endorsements for XAI
implementations. Speith [24] presents a comprehensive taxonomy of XAI that
addresses present challenges and the future. This taxonomy leverages XAI schemes
and approved taxonomies. It also leverages a decision tree to choose the best taxonomy for each application.
Raza etal. [25] presented an XAI and deep convolutional neural networks (CNN)
based an end-to-end system for Electrocardiogram (ECG)-based healthcare in a federated environment. The framework addresses challenges like data availability and privacy concerns. It effectively classies various arrhythmias using a CNN-based
autoencoder and classier. Additionally, an XAI module is introduced to interpret the
classication results, aiding clinical practitioners in making informed decisions. The
framework provides 94.5% and 98.9% accuracy for noise and clean data while arrhythmia detection. A communication cost reduction method is also suggested to enhance
privacy in the federated environment. The framework can be used for things other than
ECG classication in healthcare. However, the current work employs the federated
environment which collects the data from various servers/devices and performs the
analysis in distributed environment that resulting data poisoning attacks. Therefore,
prioritizing data integrity authentication techniques is crucial for preventing poisoning
attacks. However, this aspect of the work is still not adequately addressed. Moreover
the results are generated through homogeneous data which need to focus on heterogeneous data and the results are assessed through the DL evaluation metrics which need
to focus on XAI evaluation metric.
Saraswat etal. [1] exhibit an analysis that explores the role of XAI in Healthcare
5.0, including techniques, models, and applications. It introduces XAI basics,
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metrics, and a proposed architecture for COVID-19 segmentation and patient classication. A nomenclature of XAI in Healthcare 5.0 is provided, along with a case
study on decentralized healthcare using federated learning and XAI.The performance evaluations conrm the advantages of XAI in health settings. The study ends
with open research problems that have been learned. However, this scheme evaluates the performance through ML evaluation metrics but required to assess XAI
performance which is lacking.
Srinivasu etal. [16] proposed an enhancement in the understandability of AI
models in healthcare through Shapley Additive Explanations (SHAP) and Local
Interpretable Model-Agnostic Explanations (LIME) schemes. The paper presents
current and future XAI technologies, including study results, hurdles, and limits. It
discusses the role of XAI in healthcare including forecasting future illnesses, and
intelligent disease diagnosis. The metrics used to evaluate model explainability and
numerous tools are introduced. For better understanding, three case studies show
how XAI works, and it affects healthcare. However, the clinical decision supports
the dynamic environment where the traditional statistical metrics determines the
performance of the model and its difculties. The difculties are addressed with
human intervention in controlled manner. Hence, the XAI models require the fully
decision support schemes without the human intervention.
Fan etal. [26] show a novel multi-task learning (MTL) framework for classifying
and locating breast tumors on breast ultrasound images. The framework is made up
of three attention components that work together to enhance the way features are
represented for better classication and localization. A decoupled semi-supervised
training approach trains the imperfect datasets. This simplied system offers exibility in choosing suitable architectures for classication and lesion-aware networks
for different clinical applications. However, this scheme suffers from data-sharing
conicts and multiple data modalities.
Sutton etal. [27] leverage a sample of 8000 labeled endoscopic images from the
HyperKvasir dataset to test different CNN models. The study successfully distinguished the difference between ulcerative colitis (UC) and other diseases and forecast
the Mayo score for disease intensity. Using DenseNet121 achieves the highest accuracy (87.50%) and area under the curve (AUC) (0.90), outperforming the majority class
prediction. Gradient-weighted Class Activation Mapping (Grad- CAM) was employed
for improved visual interpretation and explainable AI.However, the proposed scheme
requires performance enhancement by expanding the dataset and its features.
Alsinglawi etal. [28] propose a machine learning-based framework for predicting Length of Staying (LOS) in lung cancer patients through EHR.The Random
Forest (RF) model was employed and achieved the best results in predicting LOS
during Intensive Care Unit (ICU) hospitalization. The Synthetic Minority
Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) methods
showed the ultimate AUC (98%) for feature selection. The amalgamation of over
and under-sampling yields similarly impressive AUC results (98% and 97%).
Further, SHAP is used to explain the RF model’s outcome and identify signicant
clinical features for predicting lung cancer LOS.However, the leveraged data set
(MIMIC-III) consist of a small sample size of lung diagnosis data that restrict the
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performance assessment of the proposed scheme, and hyper-parameter tuning is
ignored due to the small dataset.
Du etal. [29] developed an explainable clinical decision support system (CDSS)
through ML to identify the risk of women during pregnancy intervention. To prepare
the maternal features and blood biomarkers through the pregnancy exercise and
nutrition research study (PEARS) with oversample and feature selection. Further,
SHAP employed multiple models to validate the trust and acceptance for various use
cases. However, this requires validating the CDSS according to the clinical setting.
Severn etal. [30] proposed a novel pipeline radionics data SHAP to defend estimations from complicated medical images. The clinician-focused dashboard visualizes explainable ML (XML) imaging results and can be adapted to different settings.
Further, develop the application that performs prediction through genetic mutations
using MRI data from glioma patients. Thus, the proposed scheme enhances the
interpretability and acceptance of medical decision-making in clinical practice.
However, this scheme lacks the evaluation metrics for explanation quality in cases
where the prediction model estimation is unknown.
Chaddad et al. [31] explored deep radio mic forms from 3D CNN maps that
forecast immune cell markers and brain tumor patients’ survival. Findings indicate
relatives between deep amenities, immune system signs, and the survival of glioma
patients. This leads to an accurate forecast model for three immune cell signs and
survival rates. However, this model requires in depth validation by exploring the
other datasets.
Akula etal. [32] present that attention-based explanations alone are insufcient
to enhance human trust in CNN models. Instead, counterfactual explanations with
the theory-of-mind (CX-ToM) method employs fault lines, and counterfactual
explanations that identify minimal semantic-level features to alter image classication. Further, extensive experiments validate CX-ToM’s superiority over existing
XAI models.
Van der Velden etal. [33] present that deep learning gains prominence in medical
image analysis, and explainability becomes crucial. Thus, DL-based medical image
analysis through XAI and classication schemes are discussed. Further, categorizes
medical image analysis based on the XAI techniques and anatomical location, concluding with prospects for XAI in the eld.
Chaddad etal. [34] present XAI to provide interpretable justications, boosting
condence when results align with clinician expectations. However, in complex
models, explanations reect only a fraction of justication. Transfer learning and
domain adaptation enable models to be trained across multiple domains. Federated
learning protects sensitive data by updating models across sites without sharing
personal health information.
Bhattacharya etal. [35] state that the metaverse offers enhanced healthcare experiences but raises concerns regarding patient data and virtual avatars. Blockchain
can provide transparency and immutability in metaverse transactions. XAI ensures
trust in healthcare informatics. The interaction between blockchain and XAI facilitates trustworthy informatics in Healthcare 5.0 enabled by metaverse. The article
explores this interaction via a telesurgical scheme, addressing challenges and presenting experimental benets compared to traditional telesurgery systems.
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Explanation
scope Future direction
Model
specicity
aspects in XAI-determined Healthcare 5.0.
Exploring the integration of blockchain in
IoT-based medical systems for storing critical
patient data and utilizing assisted XAI modules to
provide explainability and verication of
analytical models
in-home care, human activity identication, and
novel arrhythmia patterns for diagnosis and
monitoring. Data integrity and authentication
schemes are essential because data is collected
from various sensors
S. Pulipeti et al.
sensitivity in the detection of microscopic
environment decision support
to multiple data modalities are challenging
to include larger and more diverse clinical data
and incorporating hand-crafted and texture
descriptors. Additional features are fused with
CNN features at the dense layer
Interpretation
type
A B C D E F
ECG × ✔ × ✔ ✔ ✔ Investigate the incorporation of trust and security
XAI frame
work Modalities
Author Classier
Table 1 A comparative study of XAI schemes in Healthcare 5.0
[1] CNN CAM and
Grad- CAM
SHAP CT and MRI × ✔ × ✔ ✔ × The model produces the high complexity
decoder
[20] Autoencoder-
[22] LR SHAP Nerve sparing × ✔ × ✔ ✔ × Extend the framework to anomaly detection
Heart disease data × ✔ × ✔ ✔ × Exploration is required towards dynamic
SHAP and
[25] CNN Grad- CAM ECG × ✔ × ✔ ✔ × The proposed requires an improvement in
[26] Deep neural
LIME
CAM and SAM Breast ultrasound × ✔ × ✔ ✔ × The conict in information sharing and adapting
network
attention
module
[16] Mask-
[27] CNN Grad- CAM UC images × ✔ × ✔ ✔ × To enhance performance by expanding the dataset
Explainable AI: Methods, Frameworks, andTools forHealthcare 5.0
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dataset to predict lung cancer using LOS.Further,
the DL techniques are employed to forecast LOS
in lung cancers and perform the hyper parameters
tuning
to the clinical setup
to enhance trustworthiness in forecast models,
with clinician’s feedback playing a crucial role. A
qualitative study comparing saliency maps and
SHAP feature importance can direct future
research in medical imaging explainability
cancer groupings
case studies
data from various servers/devices that resulting
data poisoning attacks. Therefore, to handle
poising attack data integrity authentication
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techniques
various XAI analyses data to anticipate the best
telesurgery use case
SHAP EHR × ✔ × ✔ ✔ × Examine the performance under the real hospital
forest
[28] Random
× ✔ × ✔ ✔ ✔ Investigate the performance of CDSS according
diabetes mellitus
SHAP MRI × ✔ × ✔ ✔ × Proving relevant metrics for explanation quality
logistic
regression
[29] SVM SHAP Gestational
[30] Penalized
× ✔ ✔ × × ✔ Expand the deep radio mic pipeline to other
MRI sequences
[31] CNN DRD Immunotherapy,
× ✔ × ✔ ✔ × The federated environment which collects the
[32] CNN CX-ToM Image recognition × ✔ × × ✔ Expand the proposed CX-ToM method to various
[34] CNN CAM Clinical reports
and images
SHAP Telesurgery × ✔ × ✔ × ✔ Expanding on real metaverse setups, compare
regression
[35] Logistic
A: Model-based; B: Post-hoc; C: Model specic; D: Model agnostic; E: Local; F: Global
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According to Table 1, the XAI-based approaches are developed through
using SHAP, Grad-CAM, and CAM; however, other XAI algorithms are essential for most of the applications. Further, none of the applications are focused to
develop the hybrid explanation scope, still, it is an open challenge for researchers.
S. Pulipeti et al.
2.1 Critical Analysis
1. The XAI-based approaches are developed through SHAP which is leverages
model agnostic and post-hoc model. However, hybrid interpretation models
have not yet been developed.
2. Most of the research work is focused on the CNN and logistic regression
schemes for classication; however, less work is focused on other types of
techniques.
3. The performance of XAI schemes is assessed with the traditional statistical
methods which are employed in ML/DL techniques. However, XAI methods
require distinct evaluation metrics for accurate assessment.
4. The federated learning schemes collect the data from various sources and
perform the analysis in distributed environment, presenting the numerous security challenges.
5. The performance of the XAI schemes often relies on the insights of data science
experts. However, incorporating the perspectives of medical experts is essential
for a comprehensive analysis.
3 Toolkits forXAI
Explainable models often complement predictive and diagnostic approaches by providing insights into the properties and components of existing models. In healthcare, XAI frameworks tend to retrot approximation methods onto original AI
models. The human focus lies more on evidence and causation rather than probabilities. Therefore, a causal explanation is more satisfying than a mere statistical representation. Therefore, this section presents the various toolkits employed in the
implementation of XAI-based applications.
Table 2 enlists the various languages used for developing XAI-based frameworks
Python and MATLAB. However, most of the researchers leveraged the Python
libraries only.
Explainable AI: Methods, Frameworks, andTools forHealthcare 5.0
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Table 2 Summary of XAI-based application tools
Reference Language Packages/software Dataset
[19] Python Scikit-learn, InterpretML Wisconsin
[20] Python PyTorch CC-CCII
[22] Python STREAM-URO framework, shap, streamlit,
joblib library
[1, 25] Python and
Tensor Flow
[27] Python and
MATLAB
[28] Python sci-kit-learn, shap library MIMIC-III
[29] Python sci-kit-learn, imbalanced-learn fancy impute,
[30] Python PyRadiomics TCGA-GBM
[32] Python EpisodicReplayMemory, ActorCritic ILSVRC2012
[31] MATLAB mdCNN MATLAB 3dMNIST
Raspberry Pi devices with B+ MIT-BIH
Tensor Flow and Keras packages HyperKvasir
xgboost, and shap libraries
Retrospective
cohort
ISRCTN29316280
dataset
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4 Future Research Directions
• Explanation techniques requiring ne-tuning of parameters depend on the interpretation type. Model-based techniques are inuenced by network ne-tuning,
while visual explanations like Grad-CAM and Deep SHAP involve parameter
choices like layer selection or background signal sampling. Perturbation-based
techniques such as occlusion sensitivity and LIME require user-dened
perturbations, and meaningful perturbation relies on user-dened techniques.
Textual explanation methods like TCAV need ne-tuning for tested concepts,
and inuence functions require dening the functions for measuring inuence in
post hoc example-based explanations [33].
• Many current XAI techniques are post hoc, likely due to their user-friendly
nature for clinicians and researchers. However, it is advisable to develop interpretable models for high-risk medical scenarios to ensure transparency and
understand ability [33].
• Vital clinical application such as MRI scan is efcient and precise reconstruction
through DL-based techniques. However, acquiring and sharing large amounts of
data is challenging due to costs and privacy regulations. Further, DL techniques
preserve the data in a centralized server which poses security and privacy risks in
critical healthcare [36, 37].
• XAI-driven Healthcare 5.0 requires investigating blockchain and XAI integration in IoT-driven healthcare environments. Using blockchain ledgers for preserving crucial patient data and aided XAI modules in explaining and verifying
analytical models. This method guarantees reliable and comprehensible analytics in decentralized healthcare [1].
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5 Conclusion
The healthcare industry is experiencing a signicant shift toward digital wellness
within the Healthcare 5.0 ecosystems. In this new landscape, decision models rely
on analytics-driven approaches that provide real-time predictions and valuable
informatics support. To accommodate this transformation, there has been a preference for white-box analytics instead of traditional black-box (BB) models. This
shift is primarily driven by the increasing importance of interpretability and concerns regarding the reliability of AI models. As a result, AI models are now incorporating XAI decision modules to address these interpretability challenges. XAI plays
a crucial role in building trust within clinical practices by enabling interpretability
and facilitating model debugging. By enhancing performance through bias reduction, XAI ensures the accuracy and reliability of AI models in healthcare applications. The proposed survey aims to shed light on the wide-ranging possibilities of
XAI within the Healthcare 5.0 framework. It emphasizes the signicant scope of
XAI in addressing the challenges and requirements in healthcare, where digital
wellness and analytics-driven decision models are key mechanisms. Further, provides the tools and libraries that are employed in the implementation of Healthcare
5.0 applications and enlist the future directions of the healthcare paradigm.
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