Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5533_Библиотеки_им_академика_М_И_Перельмана.pdf
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
translating medical terminology for medical students [19]. Using an AI workbench
platform, Toronto-based Deep Genomics developed a novel genetic target and
respective oligonucleotide drug candidate, DG12P1, to target a rare inherited variant
of Wilson’sdisease[20]. AI can detect the lead compounds and expedite the validation
of drug targets [21, 22]. AI plays a critical role in drug repurposing by predicting
interactions between drugs and targets that evade polypharmacology [21]. Within drug
discovery, AI technologies such as bioinformatics and cheminformatics models
significantly reduce the high costs and time required for drug discovery [20, 23].
9.2.7 Rehabilitation
AI has played a crucial role in the field of rehabilitation, encompassing both physical
(robotics) and virtual domains (informatics). ML, a subset of AI, is used for
perioperative medicine, brain–computer interface technology, myoelectric control,
and symbiotic neuroprosthetics. ML techniques are increasingly utilized in the
musculoskeletal domain, encompassing tasks such as patient data assessment, CDS,
and diagnostic imaging. Moreover, in the domain of therapy, an artificial cognitive
system was employed to facilitate rehabilitation exercises by analyzing signals from
machinery [24]. These advancements in technology, AI, and robotics are reshaping
conventional approaches.
9.2.8 Administrative applications
AIplaysa vital role in administrative applicationsinthehealthcare system.Inthefield of
administrative applications, AI is less revolutionary than observed in patient care;
nevertheless, it holds the capacity to yield significant efficiencies. Administrative
applications are essential since 25% of the work time for nurses is dedicated to
administrative tasks [25]. Robotic process automation (RPA) stands out as the most
dependable technology to address this challenge, offering versatile applications in
healthcare such as claims processing, clinical documentation, revenue cycle management, and medical record administration[26]. Theintegralroleof CDSin thehealthcare
system involves the management of administrative functions and offering assistance in
diagnostic coding, procedure ordering, and the triage of patients for testing. A CDS
systemwas designed to solve the inconsistencies associated with emergency department
(ED) admission and categorization of illnesses, and standardized codes were used to
represent diseases and diagnoses [27]. It contributes to improving the quality of clinical
documentation. The obstetric CDSS integrated an enhanced prompting system,
resulting in a significant improvement in documenting indications for labourinduction
and estimated fetal weight compared to a control hospital [28].
9.3 Types of AI algorithms in CDS
9.3.1 Machine learning algorithms
ML algorithms have revolutionized healthcare by enabling accurate pattern recognition and disease prediction. The evaluation of large datasets is where ML
approaches shine, which is why they are ideal for healthcare data analysis.
9-6
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
Everything from genetic information to medical pictures to patient data falls under
this category. A significant use of ML algorithms in healthcare is the detection and
prediction of disease trends [29]. Logistic regression is a popular ML method for
binary classification applications like disease prevalence prediction. A patient’s
probability of having a given ailment is determined using data inputs such as
demographics, medical history, and clinical test results. Support vector machines
(SVMs) are a powerful tool for both classification and regression. When the distance
between the classes is rather considerable, this technique performs admirably [30]. It is
possible to employ SVMs for disease identification using characteristics derived from
genetic data, medical imaging, and other patient data sources. Random forest is an
example of an ensemble learning technique that uses several decisions to improve
prediction accuracy. Its most prevalent applications in medical research include
disease categorization, patient outcome prediction, and risk factor identification
utilizing a range of input parameters. Neural networks, and more specifically deep
learning architectures such as convolutional neural networks (CNNs) and recurrent
neural networks (RNNs), are experts at dealing with complex and high-dimensional
data, such as time-series data and medical images. Tasks like medical image
identification (e.g., tumour detection in MRI or CT scans) and sickness progression
prediction are made possible by combining CNNs with RNNs, which permit
sequential data analysis. Decision trees offer a simple and understandable ML
method for categorization jobs. One typical use of these algorithms is medical decision
trees, which take patient information and utilize it to steer a chain of yes/no decisions
that ultimately lead to a diagnosis and treatment strategy. K-Nearest Neighbors (kNN) is a non-parametric method that has been widely used for regression and
classification. For patient categorization, it employs the feature space’smajorityclass
and k-nearest neighbours. Disease identification and patient risk categorization are
only two of the many medical sectors where k-NN has proven useful.
9.3.2 Bayesian Gaussian regression
The premise upon which the Naive Bayes approach rests is the independence of
attributes. Gaussian Naive Bayes is extensively used in medical diagnosis issues
requiring continuous features due to its efficacy with high-dimensional data. Long
Short-Term Memory (LSTM) RNNs excel in detecting patterns of interdependence
in sequential data that span several time periods. They are utilized in a time-series
analysis of patient data, which is employed to predict the progression of a disease or
the effectiveness of a treatment. These ML algorithms, when combined with other
state-of-the-art approaches, aid in the development of reliable CDS systems and
predictive models for use in illness diagnosis, prognosis, and treatment planning.
Their dynamic application in healthcare is opening up new avenues for improved
patient care and more streamlined medical processes [31].
9.4 Supervised learning
Supervised AI learning is a powerful approach for diagnosis and treatment prediction
in healthcare. It involves training AI models on labelled data, where the input features
9-7
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
represent patient information, and the corresponding output labels indicate the
diagnosis or treatment outcomes. The trained AI models can then generalize patterns
from the labelled data to make predictions on new, unseen patient cases. Here’show
supervised AI learning is applied in diagnosis and treatment prediction.
9.4.1 Diagnosis and treatment prediction
Data collection, pertinent patient data, such as demographics, medical history, clinical
test findings, imaging [32] and other diagnostic information, is gathered and associated
with the respective diagnosis. Feature engineering involves preprocessing and transforming acquired data in a manner appropriate for training AI models. Feature
engineering is the process of choosing and extracting the most pertinent traits to assist
in precise diagnosis [33]. Model training involves using labelled data to train a
supervised AI model, such as logistic regression, SVMs, random forest, or neural
networks. The model is trained to correlate input characteristics with an accurate
diagnosis using labelled examples. In model evaluation, the trained model is assessed for
performance using a distinct set of data (validation or test set). Metrics such as accuracy,
precision, recall, and F1-score are employed to assess the model’s diagnostic performance. Once the model is shown to be accurate and dependable, it may be used to make
diagnoses on new patient data. The model uses the patient’s input characteristics to
estimate the diagnosis and treatment. Clinical decision-making might be greatly
improved by supervised AI learning for diagnosis and therapy prediction. These AI
models can analyse large volumes of patient data and learn from past instances to help
doctors make better diagnoses and more tailored treatment plans. If we want accurate
and trustworthy predictions in the actual world of healthcare, we need to train our
models on high-quality data that is representative of the community of patients.
9.5 Unsupervised learning
The use of unlabelled data in unsupervised AI learning allows for the efficient
identification of patterns and the gathering of patient data. While supervised
learning entails labelling examples for the AI model to learn from, unsupervised
learning seeks to discover structures, similarities, and patterns in the data on its own.
With the use of unsupervised AI learning, patterns may be discovered and patient
records can be organized. A lot of different kinds of information can be collected
and set up to be used in data preparation. This includes personal information about
patients, health records, test results, and details about their treatments. We can be
sure that the data is ready for unsupervised learning after doing this. As part of data
preparation, you may have to deal with missing numbers, scale features, and get rid
of noise. We could use a number of unsupervised AI methods, like GMM,
Hierarchical Clustering, K-means, and Density-Based Spatial Clustering of
Applications with Noise (DBSCAN), to put together patient data [34]. The
algorithm to use is based on the qualities of the data and the goals of the study.
When the chosen unstructured AI model is run on the patient data, it groups patients
who have similar traits. It is made up of patients whose data shows trends or traits
that are similar to those in other clusters. Once the groups have been made, the next
9-8
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
step is to look through them and see if there are any trends. The process involves
looking for patterns and differences between the patients in each cluster by looking
at their traits and characteristics. Patterns and observations can be found by
breaking down the types of patients in each group. These patterns might help us
figure out how diseases start, how treatments work, or how certain risk factors
impact different groups of patients. There are many ways that these trends can be
used to help people. Clustering data can be used by healthcare workers for many
things, including risk stratification, finding the right people for a clinical study,
customizing medication plans, and spotting early signs of sickness progression.
Visualization tools like scatter plots, heatmaps, and dimensionality reduction
methods (such as t-SNE) can help people understand the structure of the patient
data better by showing the groups visually.
9.6 Deep learning and neural networks
Unsupervised AI learning is effective when data doesn’t have clear titles or when
data structure is needed to address an issue. This technology helps clinicians make
fact-based judgments by detecting patterns and relationships that are hard to find by
hand. It enhances patient care too. Be careful when reviewing and adding to the data
to ensure the grouping appropriately reveals essential and clinically relevant
patterns. Deep learning, the most advanced AI technology, can find patterns in
enormous datasets. This makes it beneficial for patient and illness research and
analysis. Because they can organize raw data into representations and features, deep
learning models, especially deep learning neural networks, can operate with large,
multidimensional datasets. Deep AI learning can help identify essential information
in complex patient and sickness data.
Deep learning can handle medical photos, genetic data, EHRs, and time-series
data like vital signs. DNNs can handle various dimensions and modalities of raw
data. CNNs can excel at looking at pictures [35]. They immediately learn
hierarchical elements like edges, textures, and forms from medical photos. This
allows them to split images, recognize objects, and categorize illnesses (such as MRI
scans for cancers). Autoencoders are uncontrolled deep learning models used to
decrease dimensionality and extract features. They may be able to simplify patient
data without losing critical information if they learn to shorten it. Training deep
learning models requires a lot of labelled data, which healthcare organizations often
lack. Transfer learning can enhance trained models using ImageNet. Medical jobs
that require a lot of unlabelled data benefit from this. This model training method is
quicker and more effective.
Multimodal data fusion teaches deep learning models to manage many patient
data types. Genetic data, medical images, and EHRs are examples. Multimodal data
fusion might illuminate complicated data relationships, providing a better view of
individuals’ ailments and features [36]. Since deep learning models are difficult,
researchers are still trying to explain how they work. To feel more secure and seek
treatment aid, folks must simplify models. Deep AI learning can enhance diagnosis,
uncover novel biomarkers, and personalize drugs in healthcare. Data privacy, model
9-9
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
interpretability, and morality must be considered while using deep AI learning in
healthcare. This research might aid patients by assessing their conditions and
creating individualized treatment strategies.
9.7 Natural language processing (NLP) techniques
Neural networks, specifically CNNs for image processing and RNNs for signal
processing, have emerged as powerful tools in healthcare. These deep learning
architectures have revolutionized medical image analysis and signal-processing
tasks, contributing to improved diagnostics, disease detection, and patient care.
Here’s how neural networks are applied in image and signal processing in
healthcare.
9.7.1 Convolutional neural networks (CNNs) for medical image analysis
CNNs are specifically designed for processing images and have shown remarkable
success in various medical imaging tasks:
Image segmentation: CNNs can segment medical images, such as MRI or CT
scans, to identify and delineate regions of interest. This is crucial for tumour
detection, organ segmentation, and treatment planning [35].
Disease classification: CNNs can classify medical images into different disease
categories, such as classifying skin lesions for melanoma detection or diagnosing
various lung diseases from chest x-rays.
Object detection: CNNs can detect and localize specific objects or abnormalities
within medical images, such as identifying retinal lesions in fundus images for
diabetic retinopathy screening.
Image super-resolution: CNNs can enhance the resolution of medical images,
enabling more precise diagnoses and treatment planning.
9.7.2 Recurrent neural networks (RNNs) for signal processing
RNNs are specialized for processing sequential data, making them ideal for
analyzing time-series signals in healthcare:
Vital signs monitoring: RNNs can process continuous physiological signals, such
as heart rate, blood pressure, and ECGs, to monitor patient health and detect
anomalies or arrhythmias.
Disease progression prediction: RNNs can analyze time-series patient data to
predict disease progression and patient outcomes, aiding in treatment planning and
decision-making.
Natural language processing (NLP) for medical reports: RNNs, particularly
LSTM networks, can process medical reports, clinical notes, and EHRs to extract
valuable information and insights.
Multimodal data fusion: In some cases, neural networks can be designed to handle
both image and signal data simultaneously, known as multimodal data fusion.
Combining medical imaging and vital signs: Multimodal neural networks can
integrate medical imaging data with time-series vital signs data for a more
comprehensive patient analysis.
9-10
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
Multimodal disease diagnosis: Combining data from different modalities (e.g.,
imaging, genomics, clinical data) can enhance disease diagnosis and treatment
prediction, particularly in complex diseases.
Neural networks in image and signal processing have the potential to significantly
impact healthcare, facilitating early disease detection, improving diagnostics accuracy,
and enabling personalized treatment strategies. However, the successful application of
neural networks requires high-quality and large datasets, collaboration between medical
experts and data scientists, and robust model validation and interpretation to ensure the
reliable and responsible use of these technologies in clinical settings. As research in this
field progresses, we can expect continued advancements in medical image and signal
processing, leading to better patient care and outcomes, such AI based implementations
are listed in table 9.1 representing the AI tools used in healthcare.
Table 9.1. Representing several AI-based implementations in the field of healthcare.
Sl.
Current AI-based clinical data
No.
support systems Application in clinical trial References
1. AI-PRS (AI-Polygenic risk scores) Optimum dose estimation and
combinations of drugs to be used
for HIV treatment
2. Bayesian network (BN) Predict PPIs (protein–protein
Interactions)
3. EHR data Detection, acquisition, and
maintenance of patients
4. AI-aided colonoscopy Detection of colorectal neoplasia [40]
5. Genome-based cardiovascular
medicine.
Used to stratify baseline BP
6. SDQ (smart data quality) AI in vaccine research [42]
7. SVM algorithm Common variable immunodeficiency
8. Clinical trial simulation (CTS) tool
Alzheimer’s disease assessment Scale-
cognitive sub-scale (ADAS-Cog)
scores
9. IDx-DR diagnostic system Detect diabetic retinopathy [45]
10 WISENSE system Esophagogastroduodenoscopy
11. Extreme gradient boosting (XGB),
logistic regression (LR), random
forest (RF), and Naive Bayes
model (NB)
12. Automated EHR phenotyping (AEP) Accurately identifies patients with
Cardiovascular diseases [41]
(CVID)
Mild-to-moderate Alzheimer’s
disease
(EGD) automatically generate
photo documentation during EGD
Clear cell renal cell carcinoma
(ccRCC) with bone metastasis
epilepsy
[37]
[38]
[39]
[43]
[44]
[46]
[47]
[48]
9-11
(Continued)
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
Table 9.1. (Continued )
Sl.
Current AI-based clinical data
No.
support systems Application in clinical trial References
13. Robot-assisted radical cystectomy
(RARC)
14. Variant combinations pathogenicity
predictor (VarCoPP)
15. Telemonitoring Collect the telephonic recorded
16. ANNs and DNNs Predict the Parkinson’s using the deep
17. Inflammatory bowel disease (IBD) AI is to identify patients for clinical
18. Colonoscopy Reduce miss rates during colonoscopy [54]
19. Depression Early detection of depression using a
20 Schizophrenia AI approach to patient selection for a
21. Asthma Asthma-guidance and prediction
22. Heart disease Identify heart diseases by auscultation [58]
23. Laparoscopic cholecystectomy Smart endoscopic surgery (SES), is a
24. Suspected cardiac chest pain RAPIDx AI used for diagnostic and
Bladder cancer [ 49]
Identifies pathogenic variant
combinations in gene pairs (for
genetic understanding of rare
diseases)
speech collected under acoustically
non-controlled conditions utilizing
different statistical machine
learning techniques and strategies.
Used in Parkinsion’s disease
learning algorithm
trials via an automated endoscopic
screening process known as AIrecruitment (AI-R).
conversational AI bot
clinical trial by identifying a
subgroup of schizophrenia with an
improved treatment effect
system (A-GPS), an Artificial
intelligence (AI)-assisted CDS tool,
in optimizing asthma management
through a randomized clinical trial
(RCT).
surgical system that uses artificial
intelligence (AI) to detect the
anatomical landmarks that expert
surgeons base on to perform
certain surgical maneuvers
prognostic assessment of suspected
cardiac chest pain
[50]
[51]
[52]
[53]
[55]
[56]
[57]
[59]
[60]
9-12
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
9.8 Current AI-based clinical data support system
9.9 Challenges and considerations
9.9.1 Current AI-based CDS systems
Several AI-based CDS systems have been successfully implemented and used in realworld healthcare settings. These systems leverage AI technologies to assist healthcare professionals in making informed and evidence-based decisions. Some
notable examples include the following.
IBM Watson for Oncology: IBM Watson for Oncology is an AI-powered CDS
system that provides treatment recommendations for cancer patients. It analyzes
large volumes of medical literature, clinical trial data, and patient records to offer
personalized treatment options based on the patient’s specific condition and genetic
profile.
Google DeepMind’s Streams: Streams is a mobile app developed by Google
DeepMind in collaboration with the National Health Service (NHS) in the UK. It
uses AI to help clinicians detect acute kidney injury by analyzing patient data and
providing real-time alerts for early intervention.
Aidoc: Aidoc is an AI-powered radiology platform that assists radiologists in
detecting and prioritizing critical findings in medical imaging studies, such as CT
scans and x-rays. The system uses deep learning algorithms to highlight abnormalities and potential pathologies.
IDx-DR: IDx-DR is an AI-based diagnostic system that detects diabetic
retinopathy from retinal images. It received US Food and Drug Administration
(FDA) approval as the first autonomous AI system to make a clinical decision
without requiring additional physician interpretation.
CLEW: CLEW is an AI-powered critical care platform that uses predictive
analytics to identify patients at risk of clinical deterioration in the intensive care unit
(ICU). The system continuously monitors vital signs and provides early warning
alerts to healthcare providers.
Tempus: Tempus is an AI-driven healthcare platform that uses ML to analyze
clinical and molecular data to inform cancer treatment decisions. It assists
oncologists in tailoring treatments based on patients’ molecular profiles and
responses to therapies.
Zebra Medical Vision: Zebra Medical Vision offers a suite of AI algorithms that
analyze medical imaging data for various conditions, including liver diseases, lung
diseases, and cardiovascular risks. It provides radiologists with additional insights
and improves diagnostic accuracy.
Medtronic’s insulin pump with auto mode: Medtronic’s AI-powered insulin pump
system automatically adjusts insulin delivery based on real-time glucose readings,
reducing the need for manual adjustments and offering better glucose control for
diabetes patients.
These examples demonstrate the growing adoption of AI-based CDS systems
across different areas of healthcare, including radiology, pathology, oncology,
9-13
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
critical care, and chronic disease management. As AI technology continues to evolve
and more research is conducted, we can expect even more innovative and impactful
AI-driven CDS solutions to emerge in real-world healthcare settings.
9.10 Regulatory and ethical issues (HIPAA, GDPR, etc)
AI-driven healthcare presents several ethical considerations and regulatory aspects
that must be carefully addressed to ensure patient safety, privacy, and
equitable access to healthcare. Some of the key ethical considerations and regulatory
aspects are as follows.
Data privacy and security: AI in healthcare requires access to sensitive patient
data, raising concerns about data privacy and security. Healthcare organizations
must implement robust security measures to protect patient data from unauthorized
access, breaches, and misuse.
Informed consent: Patients must be adequately informed about the use of AI in
their healthcare and provide informed consent for their data to be used for AI-driven
decision-making.
Transparency and explainability: AI algorithms can be complex and difficult to
interpret. Healthcare professionals need to understand the rationale behind AIdriven recommendations to trust and utilize them effectively. Transparent and
explainable AI models are essential to build trust between clinicians and AI systems.
Algorithm bias: AI models trained on biased data can perpetuate healthcare
disparities and lead to inequitable treatment recommendations for certain patient
groups. Ensuring fairness and minimizing bias in AI algorithms is crucial for
equitable patient care.
Autonomy and human oversight: AI should complement, not replace, the expertise
and judgment of healthcare professionals. Maintaining human oversight is essential
to ensure that AI-driven recommendations align with patient preferences and values.
Accountability and liability: Determining responsibility and liability in cases of
AI-driven errors or adverse outcomes can be challenging. Clear guidelines and
protocols are needed to assign responsibility and address legal considerations.
Generalizability and bias in AI models: AI models trained on specific populations
may not generalize well to other groups or regions. Efforts should be made to ensure
that AI models are diverse and representative to avoid perpetuating bias and
disparities.
Regulatory approval and validation: AI-based medical devices and CDS systems
require regulatory approvals, such as FDA clearance. Rigorous validation and
testing are essential to ensure the safety and efficacy of AI-driven healthcare
applications.
Resource allocation and accessibility: AI-driven healthcare technologies should be
accessible to all patient populations, including underserved communities. Ensuring
equitable access to AI-driven healthcare is crucial to avoid exacerbating existing
healthcare disparities.
Over-reliance on AI: Over-reliance on AI-generated recommendations without
clinical validation or human oversight may lead to suboptimal patient care.
9-14
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
Healthcare professionals should use AI as a tool to enhance decision-making, not as
a substitute for clinical judgment.
Addressing these ethical considerations and regulatory aspects requires collaboration among healthcare organizations, AI developers, policymakers, and ethicists.
Implementing responsible AI practices and adhering to established ethical guidelines
can help maximize the benefits of AI-driven healthcare while mitigating potential
risks and challenges. Striking the right balance between innovation and ethical
principles is crucial for the successful and sustainable integration of AI in healthcare.
9.11 Challenges for clinical translation
The recent spate of articles and datasets demonstrates the impressive progress made
by oncologic AI. There is a significant chasm, however, between proof of AI success
and proof of therapeutic efficacy. According to a recent meta-analysis, the use of
deep learning in medical imaging has only been tested in two published randomized
clinical studies, with just nine planned trials in total. Deep learning algorithms have
been the subject of hundreds of published researches on their effectiveness [61]. An
absence of both high-quality and massive volumes of data may be a major obstacle
to the development of clinical AI applications in cancer and healthcare generally.
Varoquaux and Cheplygina [62] and Thompson [63] are only two of many sources
that discuss the issues surrounding data collecting, transparency, bias, and reliability. The difficulty of AI models in comprehending, believing, replicating, and
applying to different contexts has also received much attention [64]. Even though
these issues are critical to the advancement of AI, we will now discuss certain
concepts unique to the clinical translation of models that have proven effective
throughout their early phases of development and assessment. These include
practicality, usability, and clinical validity. Although these considerations are often
overlooked during model building and evaluation, they are crucial for taking clinical
AI beyond the lab and into the treatment of cancer patients. According to Park et al,
the standard procedure for demonstrating a model’s clinical validity involves doing
internal validation, external validation, prospective testing, and finally, local testing
in the target population [65]. To ensure that the study can be replicated and done
correctly, one must adhere to new guidelines like FAIR data, CONSORT/
SPIRTAI, and the (still in development) TRIPOD-AI checklists [64]. With these
guidelines, we can finally standardize the processes used to create AI models and
have a yardstick by which to evaluate the quality of AI research. The majority of
published AI research makes use of a blinded test set conducted internally.
According to Kim et al, only a tiny fraction of models includes future testing or
standard comparisons with human experts, and even fewer employ an external
validation set [66]. Since the majority of AI models do not rely on hypotheses when
selecting features, real-world outcomes might vary greatly depending on how the test
data distribution differs from the training data [67]. This highlights the need to use
several external assessment sets. Edge cases, or scenarios not well represented in the
training data, might thus make predicting the model’s performance challenging [68].
In oncology, finding uncommon outcomes is crucial for safe cancer treatment, hence
9-15