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Unveil theBlack-Box Model forHealthcare eXplainable AI
https://t.me/med1917
Table 1
(continued)
Ref.
no.
and
year Aim Used method Observations
[18],
Examines how well
2022
COVID-19
applications can be
explained and
understood
[19],
Emerging technology
2022
survey for healthcare
security
[20],
This examines the
2022
approaches and
challenges of EXAI
[21],
Describes a method
2022
for gathering
information that can
be used to train an
articial intelligence
model for medical use
[22],
For ECG monitoring,
2022
the DL Cyberwin
model is presented
[23],
Propose an EXAI-
2022
assisted, unied
architecture for
COVID-19 patient
classication in
Healthcare 5.0
Feature-based
distribution, Region
identication
IoT, 5G, AI, and
big data
Decision tree
tting, functioningbased approach,
and conceptual and
result-based
approach
AI, Responsible AI Proposed ndings help pregnant ladies by
Cyberwin, DL, 6G,
and data fusion
Federated transfer
learning, 5G, AI,
XAI, and CNN
It lists several tasks, gives a summary of
recent techniques that can be explained in
imaging instances, and suggests best
practices for putting eXplainable or
interpretable AI into place. Doesn’t talk
about the AI techniques that are already
available for COVID-19 classication and
how EXAI can solve this problem. The
survey is also only about certain
healthcare situations
Delivers in-depth analysis of the IoT,
fth-generation (5G) network, AI, and big
data analytics as they pertain to the
development of a safe healthcare system
and presents a case study. Lacks a unied
method for keeping healthcare systems
safe
Allows researchers to know what the
current challenges are in EXAI and how
to solve them by using a proposed method
and a decision-t approach. Does not talk
about the different types of feature
relevance methods
providing a trust-based AI solution and
also emphasize the importance of
explanation in AI-based applications. The
geographical diversity of the participants,
their personal information, and the
COVID-19 scenarios all act as barriers to
the data collection
Proposed IoT architecture for real-time
data gathering, processing, and monitoring
patterns of ECG and activities over a
6G-enabled network with high accuracy
and reduced computation time. Does not
provide explanations for the DNN mode’s
accuracy
Use an XAI and AI-integrated architecture
that provides context for performance
measurements in a 5G network
55
(continued)
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Table 1
(continued)
Ref.
no.
and
year Aim Used method Observations
[24],
XAI in insurance Simplication
2022
[25],
Overview of XAI
2022
methods in different
nance areas
[26],
Credit assessment in
2022
banks
[27],
Aimed at detecting
2022
COVID-19 based on
lung X-rays and
provides insights on
how AI is used by
different stake holders
[28],
Discussed various XAI
2022
approaches and their
performances
methods, called
knowledge
distillation and rule
extraction
SLR process and
XAI methods in
practice
XAI, Light GBM
model, Logistic
regression
AI, XAI, decisionmaking support,
Supervised DNN
XAI, LIME, SHAP Three case studies about the role of XAI
XAI’s applications in nancial services
and its current use within the insurance
industry
Research trends and clustering existing
research in the areas of nance
Improves the interoperability and
reliability of a credit scoring model
Presents LngX as a decision support tool
and AI-based model for diagnosing
COVID-19
in the healthcare domain and their
performances are incorporated for better
comprehension
R. Aluvalu et al.
Table 2
Deep learning in healthcare applications
Application Data source DL algorithm Ref. no.
Cancer diagnosis and classication Gene expression data Deep autoencoders [29]
Alzheimer diagnosis PET scans CNN, DNN [30]
Organ segmentation Endoscopy images CNN [31]
Age of fossils RSNA pediatric bone age RCNN, ResNet V2 [32]
Fracture risk assessment MR image slices Unets [33]
Tumor detection and localization MR image slices CNN, SVM, RCNN [34]
Brain tumor MR image slices 3D CNNs [35]
RBP binding CLIP-seq Deep learning [36]
2.3 Challenges ofDeep Learning inHealthcare
Even though deep learning has shown prociency in feature extraction, recognition,
and classication, it has signicant hurdles when compared to earlier machine
learning methods. The complexity of biological data makes it difcult for people to
make sense of it on their own. However, algorithms alone, such as those used in
deep learning, are unable to provide a satisfactory interpretation with a sufcient
quality level, as DNNs lack the transparency required to identify biological
Unveil theBlack-Box Model forHealthcare eXplainable AI
https://t.me/med1917
Table 3 Image and EHR datasets
Datasets Remarks
Image
data
EHR MIMIC [48] Anonymous 40,000 patients’ health records
Brainweb [38] MRI images
MICCAI [39] Brain tumor
NIHCC [40] Chest X-rays
TCIA [41], VIA Group
[42]
OASIS [43] Brain images
ADNI [44] Alzheimer’s disease
FITBIR [45] Brain injury
STARE [46] Retinal images
MIDAS [47] Brain images
i2b2 [49] 1500 patients’ health records
Health data [50] Data from the US Federal Government
BCHC data [51] Data from 26 cities
HMD [52] Data on human mortality
MHealth data [53] Body movement and physical activity
SEER [54] Cancer database
LSDB [55] Life sciences data
Lungs images
database
57
connections [56]. To perform a comprehensive study of a particular set of biological
data, they need both human and computational input. This is termed a “black box”
problem. In addition, DL algorithms need a lot of training data, which isn’t always
readily available. Overtting may occur when insufcient training data is available
and the test error is high even though the training error is low. It’s also not always
clear which DNN variant is best suited to a given problem [57].
It is not always easy to decide which architecture to choose, especially with the
addition of new ones on a regular basis, but there are methods to help in this selection, such as hyperparameter optimization techniques. DNNs have a data training
procedure that is time-consuming and requires trained humans, despite the fact that
computational techniques often reduce the cost of analyzing data and save time. A
number of critical research topics and their associated solutions were deemed
beyond the capabilities of deep learning. To begin, many of the high-level representations produced by deep learning are difcult to understand. In addition, there are
sometimes no procedures to apply adjustments in the event of a classication problem [58].
And deep learning isn’t appropriate for all types of diseases, especially rare disorders. There is also evidence that DNNs can be easily misled into obtaining misclassied information by making very small modications to the input.
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3 eXplainable AI inHealthcare
There are a lot of ways that AI could help in healthcare, but there are even more
ways that an untrusted AI system could hurt. It goes without saying that the decisions made by AI models to help doctors classify critical diseases using structured
and unstructured data like medical imaging have far-reaching effects. If an AI system makes a prediction and also explains why it came to that conclusion, it will be
much more useful than a system that makes a prediction but leaves it up to the doctors (with or without AI decisions) to gure out if the prediction is accurate and
trustworthy.
3.1 Transition fromHealthcare 1.0 to6.0
Healthcare has undergone numerous changes over time. The timeline events of XAI
are shown in Fig.2. Healthcare 1.0 describes the traditional arrangement between a
doctor and a patient. In this scenario, the patient goes to the clinic to consult with
the doctor and the rest of the staff. Following a thorough consultation, diagnostic
testing, and treatment plan, the clinician may recommend further care, such as a
referral to a specialist, for further evaluation. This theory has been widely used in
the medical eld for centuries.
The use of test equipment (like CT scans and MRIs), monitoring devices (like
oximeters), and life-support equipment has increased in hospitals. This progress is
considered as Healthcare 2.0.
Centralized, computer-based information is maintained for the patients to provide service and care across multiple units of the organization. Electronic health
records are also useful for providing treatment in online mode, which has proven
more useful during COVID-19. This revolution is seen in Healthcare 3.0.
Fig. 2 Timeline events of XAI
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In Healthcare 4.0, DARPA has given XAI, which has gained more importance in
the healthcare eld. A personalized prescription is given after diagnosing the disease. Smart devices are used for monitoring patients’ health conditions.
In the fth healthcare revolution, cyber physical systems are equipped with
RFID, sensors, etc. Wearable devices and medical robots gained more usage. The
data collected from these smart devices can be stored in the cloud. Data analysis,
DL, and ML techniques can be applied to this data, which helps in proactive treatment like disease prediction, prevention, personalized treatment, and prescription. It
is more useful for patient-centric care [59].
In the upcoming Healthcare 6.0, XAI helps in real-time data collection with
zero-touch service and network management (ZSM). Also ensures transparency and
authenticity.
In healthcare, people’s lives are at stake, so XAI is very important. Table4 gives
the summarized work in the healthcare domain.
Table 4 Summarized work done in the healthcare domain
Ref.
no. Objective Duration Company Outcome
[60] Integrating XAI to better
understand the DL
algorithms when
combining low level
features with high level
[61] A prototype created by
IBM and Geisinger Health
System determines the
severity among patients
with new COVID-19 and
incorporates an AI model
[62] In this project, focused on
the XAI module for cancer
prediction and its
signicance in clinical
analysis
[63] The project streamlines
XAI usage in healthcare
and creates models and
frameworks to test XAI
results over DL models
[64] Based on certain MRI
images, the XAI module
uses a CNN model to nd
cancer tumors. Affected
areas would be marked and
separated
2022–
present
2021–
present
2020–2023 Imperial
2021–
present
2019–2022 SAS Analytics
WIMMICS
with other six
universities of
Europe
IBM XAI model to study the
College, UK,
London
Macquarie
University,
Sydney,
Australia
and Solutions
A dialogue-based module will
be used to gather data from
patients and offer
comprehensible analyses and
forecasts that support the
medical diagnosis
COVID-19 impact
Analysis of XAI as an
interpretable method for
cancer prediction
Tools for validation over the
most common healthcare
datasets
XAI analysis on MRI images
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3.2 Questionnaire andProposed Solution
For centuries, mathematicians and statisticians have extensively researched classical machine learning methods such as linear regression, decision trees, and Bayesian
networks. These algorithms were created long before computers and are relatively
simple to understand. It is easy to come up with explanations when you make a decision using one of these conventional techniques. They did, however, only attain
precision up to a certain point. Our conventional methods were therefore very explicable but only somewhat effective.
XAI refers to an algorithm’s capacity to articulate its justication and identify
the advantages and disadvantages of its decision-making process. XAI can provide
insights into the potential future behavior of algorithms. Discussions of corporate
governance and legislation are beginning to highlight the signicance of this new
potential, which has numerous ethical, social, and legal ramications. The research
questions raised and the proposed solutions are given in Table5.
3.3 Step-by-Step Process ofXAI
The term “smart healthcare” is used to describe the integration of modern tools like
the cloud, IoT, and AI into health systems to improve the quality of care while also
making it more accessible and adaptable to individual needs. In order to empower
people to take care of their health, these innovations make it possible to monitor
Table 5 Questionnaire table for XAI
Research questionnaire Proposed solution
Is there a role for XAI in medical? To gain an awareness of the critical situation as well as
Does every AI model need to be
eXplainable?
What are the drawbacks of the
current AI model, and how XAI
helps to meet the necessary trust
standards?
What are the obstacles and
unanswered questions regarding the
integration of XAI into diverse
applications?
How accurate/precise/reliable are the
predictions of XAI?
How exactly does XAI contribute to
better healthcare?
the capabilities of XAI in order to raise the level of trust
in AI ndings as well as their overall efciency
No; based on application
DARPA says that XAI is required for elds like
transportation, security, medicine etc.
The purpose of this research is to examine and
implement XAI features into popular black-box models
in the healthcare industry so as to increase user
condence and fulll user needs
To formulate open problems and research for AI system
interpretability
Users will have more faith in the model’s conclusions
and will be able to more easily identify model aws and
data bases with an eXplainable AI
To imagine XAI and other AI technologies providing
healthcare privacy through a use case scenario
k
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pulse rate in real time with the help of healthcare apps on smartphones and wearable
devices. Health data gathered at the individual level can be used for screening, early
disease detection, and treatment planning.
Figure 3 shows the step-by-step process of XAI; the initially selected AI model
is applied to a health dataset. The output is analyzed with the help of clinical knowledge. If the prediction is incorrect, the AI model will again be applied; otherwise,
Training Data
New AI Model
eXplainable AI Model
Incorrect
Fig. 3 Flowchart of XAI
Explanation Interface
Clinical
Knowledge
Predictions
Correct
Insight and
Recommendations
Correct/
Incorrect
User Feedbac
on AI Model
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recommendations are generated. In cases of accurate or inaccurate results, valuable
user feedback on the model is fed to the XAI model [65].
3.4 XAI Stages
The various stages of XAI are shown in Fig.4. In stages 1 and 2, the goal is to help
people understand the framework and how AI techniques work. This lets people
think critically about how AI methods work and determine whether or not to accept
their results, predictions, or recommendations. To do this, we must design business
logic that applies the same “reasoning” automatically. Stage 3 focuses on facilitating interoperability between AIs and other software, particularly business logicbased software [66].
E
x
p
l
a
i
n
a
b
l
e
A
I
T
e
c
h
n
i
q
u
e
s
EXplainable
Decision Process
EXplainable
Decision
EXplainable
Building Process
Stage 3
Stage 2
Stage 1
Fully explainable
models (Decision
Tree, Rule Set,
Linear Regression)
Model explainability
techniques
(SHAP, LIME)
Visualization &
offline
Debugging
techniques
Fig. 4 Stages of XAI
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3.5 Traditional Programming Vs ML Vs XAI
In traditional programming, a problem is solved by designing logic or algorithms.
This logic is applied to the input, and the output is computed. It is a manual process;
the programmer develops the program or logic. Whereas in machine learning, a
model is built from the data that is input, and the output is fed to the algorithm to
create a program. However, XAI models help humans understand the AI model and
provide feedback. The comparison of traditional programming, ML, and XAI is
given in Fig.5.
Traditional Programming
Input
Data
Computer
Program
Machine Learning
Output
Training
Data
Output
EXplainable AI
Training
Data
Fig. 5 Comparison of traditional programming, ML, and XAI
Computer
New ML
Process
Feedback to AI model
EXplainable
AI model
Learned
Function/Program
EXplainable
Interface
Human
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4 Real-World Applications ofeXplainable AI
inVarious Domains
In this section, we will discuss the use of XAI in various applications. Figure6
depicts the specic domains in which XAI may be used to promote AI analytics as
a possible and understandable method.
In insurance companies, XAI plays a dominant role in customer retention, which
means one way to save money is to retain existing customers instead of getting new
ones. XAI can tell when a customer will leave and why. Next is claim management.
If you give a claim to a customer without a good reason, the customer may have a
bad experience. Customers are more likely to be happy if they understand the result.
One more is insurance pricing; here, the cost of insurance depends on a number of
things. With XAI, customers may better comprehend insurance pricing uctuations
and be informed about the changes [24].
Insurance
Pricing
Customer
Retention
Fraud
Detection
Claims
Management
AI-assisted
Drug
Payment
Exceptions
Detection
Applications of EXplainable AI
Banking
customer
engagement
Robo-
advisors
Data driven
Learning
models
Transport
Health Care
Finance
Military
Internet
Application
Fig. 6 Applications of XAI
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