- Email: [email protected]
Fundamentals in Artificial Intelligence for Vascular Surgeons
Journal Pre-proof Fundamentals in artificial intelligence for vascular surgeons Juliette Raffort, Cédric Adam, Marion Carrier, Fabien Lareyre PII:
S0890-5096(19)31033-7
DOI:
https://doi.org/10.1016/j.avsg.2019.11.037
Reference:
AVSG 4802
To appear in:
Annals of Vascular Surgery
Received Date: 17 October 2019 Revised Date:
17 November 2019
Accepted Date: 21 November 2019
Please cite this article as: Raffort J, Adam C, Carrier M, Lareyre F, Fundamentals in artificial intelligence for vascular surgeons, Annals of Vascular Surgery (2020), doi: https://doi.org/10.1016/ j.avsg.2019.11.037. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Elsevier Inc. All rights reserved.
Fundamentals in artificial intelligence for vascular surgeons
1 2 3
Juliette Raffort 1,2, Cédric Adam 3, Marion Carrier 3, Fabien Lareyre 2,4
4 5
1
Clinical Chemistry Laboratory, University Hospital of Nice, France
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2
Université Côte d’Azur, CHU, Inserm U1065, C3M, Nice, France
7
3
Laboratory of Applied Mathematics and Computer Science (MICS), CentraleSupélec,
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Université Paris-Saclay, France
9
4
Department of Vascular Surgery, University Hospital of Nice, France
10 11 12
Correspondence to be addressed to:
13
Juliette Raffort, MD, PhD
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Department of Clinical Biochemistry, University Hospital of Nice, France
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30 Avenue de la Voie Romaine, 06001 Nice, France
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Telephone number: +33 (0)4 92 03 87 90
17
Email address: [email protected]
18 19
Keywords: artificial intelligence, machine learning, deep learning, big data
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Abstract
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Artificial intelligence (AI) corresponds to a broad discipline that aims to design systems
28
which display properties of human intelligence. While it has led to many advances and
29
applications in daily life, its introduction in medicine is still in its infancy. AI has created
30
interesting perspectives for medical research and clinical practice but has been sometimes
31
associated with hype leading to a misunderstanding of its real capabilities. Here, we aim to
32
introduce the fundamentals notions of AI and to bring an overview of its potential
33
applications for medical and surgical practice. In the limelight of current knowledge, limits
34
and challenges to face as well as future directions are discussed.
35 36 37
Introduction
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Since its first introduction at the Darmouth College conference in 1956, significant advances
39
in artificial intelligence (AI) have been achieved leading to applications in daily life in various
40
fields from industry to finance, education, transport, marketing, media, telecommunications or
41
computer science. The earliest work of AI in medicine arose in the 1970’s and with the
42
development of machine learning techniques, AI has raised promises and hopes for
43
applications in clinical practice. On the downside, AI has created hype and ideas derived from
44
fantasy and imaginary world, sometimes leading to a misuse or a misunderstanding of the real
45
capabilities of AI systems for healthcare. In this report, we aim to introduce the fundamental
46
notions regrouped under the generic term “AI” and focus on the main techniques used for
47
medical applications. Potential applications for medical and surgical practice are discussed,
48
along with the current limits and future directions.
49 50
Fundamentals of AI in medicine
2
51
Terminology
52
AI corresponds to a broad discipline that aims to understand and design systems that display
53
properties of human intelligence, such as reasoning, learning, adaptation, interaction or
54
sensory understanding (1). It is an inter-disciplinary approach using principles and devices in
55
a variety of fields such as computation, mathematics, logic, mechanics, or biology to
56
understand, model or replicate intelligence and cognitive processes (2). In practical terms, AI
57
can be considered as the ability of a machine or a device to make autonomous decision based
58
on data collected (3). The terms related to AI and their hierarchy are summarized in Figure 1.
59
AI includes various branches such as robotics (applications of AI for motion), vision
60
(applications for videos or images) or natural language processing (applications of AI for
61
speech or written language).
62
Machine learning (ML) is a sub-discipline of AI where a technique ifs used to give the
63
machine the ability to learn (3). ML regroups a set of algorithms that are used to solve AI
64
problems (Figure 1). The technique is able to identify patterns from large datasets without
65
being explicitly programmed and without any a priori assumption. Input and output data are
66
provided and the machine determines the process by which the given input produces the given
67
output data. The process can then predict the unknown output when new input data is
68
provided (4). Three main ways for a machine to learn can be distinguished including
69
supervised, unsupervised and reinforcement learning.
70
In supervised learning, labelled inputs and outputs are provided for training. The program is
71
trained to learn a function that maps an input to an output of interest in a dataset that has been
72
labeled by a human supervisor. Once the associations have been identified based on existing
73
data and the inferred function created, it can be used to predict and map new data (5, 6). The
74
algorithm generalizes from the training data to unseen and new situations. This method is
3
75
usually used to predict or classify future events, or to find which variables are most relevant
76
to the outcomes (7).
77
On the opposite, unsupervised learning does not require any labelled or annotated data. It
78
helps to find previously unknown patterns in datasets without pre-existing labels instead of
79
trying to fit an input to an output (3). The aim is to uncover hidden patterns and natural
80
structure in dataset (8). It allows to explore complex relationships among variables in datasets
81
without any a priori assumptions.
82
Finally, reinforcement learning trains computer programs to make decisions and take actions
83
based on their ability to maximize a defined reward (5). It does not require labelled data. The
84
aim of the program is to collect as much reward as possible, finding a balance between
85
exploration and exploitation of current knowledge. The program chooses any action as a
86
function of the history and reward. This approach has been inspired from behavioral
87
psychology such as the operant conditioning and is useful to develop automated predictions or
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actions.
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Deep learning (DL) is a specific type of ML method which allows a machine to be fed by a
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large quantity of raw data and to discover the representations necessary for detection and
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classification (5). DL is a class of ML algorithms that uses multiple layers to progressively
92
extract higher level features from the raw input. It regroups computational models that are
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composed of multiple processing layers to transform the data and amplify the aspects of the
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input which are important for discrimination and suppress irrelevant variations (5, 9). The
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development of DL algorithms has led to many advances in various fields including speech
96
recognition, visual object recognition and detection, drug discovery or genomics (10).
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Numerous DL algorithms exist, among which Artificial Neural Network (ANN) and
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Convolutional Neural Network (CNN) are the most commonly used for medical applications.
4
99
ANN are inspired by biological nervous system in which neurons transmit electrical signals
100
from one end to the other. Each neuron is connected to multiple other neurons, organized in
101
layers and receive input data, process it to give an output (11) The first layer is called the
102
“input layer” and the last layer the “output layer”. The “hidden layers” between are often
103
called “black box” as it is unknown how the neural network derived a specific result. During
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the learning process, the connection strength between the neurons is adapted by the
105
applications of weights which weaken or enhance the signal. The updating of the weights is
106
determined using the back-propagation method of the error estimated between the predicted
107
outcome and the correct output.
108
Convolutional neural network (CNN) is often applied to image processing and enables
109
detection, segmentation and recognition of objects and regions within images (10). It is
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composed of a multilayered network with mathematical model simulating some of the
111
properties of the visual cortex. The neurons process portions of the input image and provide a
112
filtered representation of the original image. The process is repeated in each layer until the
113
final input.
114 115 116
Overview of AI in Medicine
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Since the earliest work on AI in medicine in the 1970’s (12), advances of technology and
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computational methods has led to a growing interest for potential applications in both medical
119
research and clinical practice (Figure 1). According to Vuong et al., the use of AI in medicine
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can schematically be categorized in to two main branches: the physical branch which includes
121
the development of assistive robots for care, surgery or drug delivery, and the virtual branch
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including the development of informatics approach and expert systems (13).
5
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The number of publications of AI in medicine has increased exponentially over the past
124
decade (14, 15) . The total number of published paper on the topic in the Web of Science
125
(WOS) database was estimated to be 27 451 at the end of the year 2018, with 61% of the
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articles published between 2014 and 2018 (14). The most prolific countries in AI in health
127
and medicine were the United States, accounting for approximately of 30.8% of the published
128
articles, followed by China, European countries and India (14). France was ranked at the 6th
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place with a total number of publication of 1308 (3.8%) at the end of 2018. Interestingly,
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trends have been observed regarding the topic studied and the potential applications of AI in
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medicine (13). Western countries tend to have a clinically-focus approach with projects
132
oriented toward specific diseases such as cancer or cardiovascular diseases. Low and middle-
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income countries rather develop applications oriented toward the public healthcare sector
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(13).
135
There is a growing interest of applications of AI in medicine involving all medical and
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surgical specialties. While there is a large number of publications on the topic related to
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cancerology, radiology, cardiology or even neurology, AI is less reported in vascular disease
138
management (15). Nevertheless, the increase of AI-related publications for vascular disease
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has been estimated to be 147% over a 17-year evaluation period (2000- 2016), suggesting a
140
real potential for a future use in medical practice.
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Concomitantly with the raise of published articles, the number of registered clinical trials has
142
drastically increased over the past few years, demonstrating the renewed interest toward
143
applications of AI for clinical practice (16). Finally, the venture capital backed for healthcare
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AI startups has raised among the past few years, suggesting that AI-driven products and
145
technologies are to be expected in the next future (17).
146
Applications of AI in cardiovascular diseases and vascular surgery
147
Type of medical data
6
148
A wide range of medical data can be used to develop AI algorithms and the term “big data”
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corresponds to a large amount of collected data. Electronic health records have been
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extensively used as they contain valuable information including structured data (such as
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diagnosis, treatments or laboratory tests) and unstructured data (such as free-text clinical
152
notes) (9, 18). While health electronic reports present the advantage to be easily available,
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many challenges remain for developing AI algorithms. Data are often heterogeneous and
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sparse, with missing information. Besides, the coding systems used in medical records vary
155
between institutions all over the world (18). In addition to health records, medical images are
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a major source of data useful to assess the diagnosis, the prognosis and the therapeutic
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response of patients. ML and DL approaches have been widely developed in cardiovascular
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imaging to facilitate image segmentation and classification (3, 7, 19). Physiological data (e.g.
159
electrocardiograms) can also be analyzed and several AI methods have been applied to help in
160
the interpretation (20). Cardiovascular diseases are impacted by environmental conditions and
161
behavioral data can be used to assess the link between the lifestyle and health of patients. This
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can include video and conversational data, social media or mobile sensor data (18). At last, AI
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can be applied in medical research settings for the analysis and the discovery of patterns in
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huge amounts of biological data such as genomic, transcriptomic or proteomic (9). Published
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data from previous work are also a major source of information in both fundamental and
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clinical research. AI approaches can be used to extract useful information from biomedical
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literature data (21). Natural language processing (NLP) is a subfield of AI which enable a
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computer program to understand human language. Such systems are useful to automatically
169
identify keywords and relevant information in large medical database. Finally, AI approaches
170
have also been developed to facilitate clinical trials design and data mining (22, 23). They
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may be useful to optimize the power of clinical trials and facilitate the discrimination of
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inclusion and exclusion criteria.
7
173 174
Technical consideration for deep learning
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While ML initially required huge computational power, the development of Graphical
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Processing Unit (GPU) has allowed DL to be performed on desktop machines (6). The
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selection of an appropriate software package and the data preparation is a mandatory step.
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This involves feature selection and treatment of missing data. The raw data are transformed
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into features that are clinically relevant to reduce dimensionality of the data. Feature selection
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is performed by the algorithm and allows to treat a large volume of complex data (24).
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Missing data can potentially affect the performances to learn and their identification is crucial
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to limit their impact by inserting labels or using auto-encoders (24).
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Numerous DL algorithms exist and the choice of the most appropriate algorithm depends on
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the aim of the study and dataset available. Once the algorithm has been defined, a large
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amount of data is necessary during the training of the model to minimize bias due to over-
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fitting (6). Indeed, when a dataset is imbalanced, predictions are biased towards the majority
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samples. One method used to reduce the over-fitting problem and improve classification
188
accuracy is to augment the data. A key point in evaluating an AI algorithm accuracy is
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external validation, which consists in testing the algorithm when new data are fed into the
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model (24). AI technologies enable to search for patterns in given data without any
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assumptions and understanding of concepts and principles, which make them strongly
192
dependent on the training data. Data used for validation should be collected independently
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from the training dataset to assess the generalizability of the model (25).
194 195
Applications of AI in vascular surgery
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Several applications derived from AI are to be expected in the healthcare information system
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and the management of clinical data registries. Such approaches may in term allow the
8
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analysis and the identification of patterns in big data including clinical, biological and
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imaging results (26). Surgeons play a central role in clinical data registries and a close
200
collaboration with data scientists would allow to optimize the relevance and the efficiency of
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heath information programs and medical registries. Such advances could lead to various
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perspectives in public healthcare surveillance as well as in medical research.
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In addition to management of medical data, AI techniques are currently being developed
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toward helping in the diagnosis and clinical decision making. In vascular surgery, imaging is
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a key step in the care provided to patients, allowing to confirm the diagnosis, evaluate the
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prognosis and plan the surgical intervention. Vascular segmentation is challenging as vessels
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exhibit high variability of morphology, size and curvature. AI techniques can help to improve
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image segmentation, pattern recognition and could be useful to automatically perform
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repetitive tasks, improving the reproducibility and reducing the computational time. As an
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example, several AI-derived methods have been applied to improve aortic aneurysm
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segmentation allowing a deep characterization of the aneurysm geometry and morphology
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(27-31). ML was also used to develop fully automated pipelines to detect and measure
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vascular calcifications from CT-scan images (32, 33).
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Computational programs were developed for image segmentation and risk stratification in
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patients with carotid artery stenosis (34-36). AI offers interesting perspectives for image
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segmentation, automation, analysis of data from health medical records, facilitating and
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improving data acquisition and quantitative measurements in a large dataset of patients. A
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combination of these methods may help to better evaluate the risk of patients and the post-
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operative outcomes. As an example, several DL algorithms have been developed to evaluate
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the risk of aortic aneurysm growth and rupture or to predict the outcomes after aneurysm
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surgical repair (37-41) (42). There is currently no clear consensus on how to manage complex
222
aortic aneurysms such as juxta-renal AAA. Recent advances in endovascular technology have
9
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led to the availability of multiple devices including fenestrated or chimney-endografts. A
224
recent study highlighted significant variations in the management of juxta-renal AAA where
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either continue surveillance, endovascular or open repair were preconized by vascular
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surgeons for the same patient depending on the country (43). This underlines a real need to
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develop new tools to help surgeons to better evaluate the most adapted therapeutic approach.
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By enabling the development of multi-variable scores combining clinical, biological and
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imaging characteristics, AI may allow to classify the patient status, better assess the risk of
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per and post-operative complications and guide surgeons to choose the most appropriate
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surgical technique, especially in case of complex aneurysm. AI-derived methods could help to
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improve the evaluation of the risks and prognosis of patients, facilitate the pre-operative
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planning and propose a more personalized therapeutic approach. Finally, AI could also be
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applied for medical training and education by enabling simulation of clinical situations. As an
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example, virtual reality simulators have been developed and could be used for the training of
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young surgeons on the basic endovascular procedures (44, 45).
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Current limits and future directions
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While AI-derived technologies offer potential interesting applications for medical research
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and clinical practice, several challenges remain. The first technical concern relates to the data.
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DL requires the availability of huge amount of data to train an effective and robust model.
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Medical data are very heterogeneous, can be incomplete or inaccurate. They can also evolve
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over time and be hardly interpretable (9). In addition, data are generated by various
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manufacturers, are stored in many distinct registries, leading to a wide heterogeneity of
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quality, formats, resolutions, dimensions and scales. A huge effort of standardization is
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necessary to develop large multicenter databases. With the increase number of AI in health
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models, the privacy and the security of data is another challenge to face for data sharing (18).
10
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Another concern regarding AI algorithms is their external validation to evaluate the
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generalizability of the results. Ideally, multicenter registries should be used to take into
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account a broad spectrum of patient’s demographics and the replication of the results by other
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groups should be evaluated. The interpretability of AI algorithms can also be challenging as
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techniques such as ANN are based on a “black box” design, with little ability to identify how
253
and why such patterns were identified by the computer (26). The algorithms do not give any
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causal relationship. The accountability of algorithms and the verifiability of analyses
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generated is a major challenge for their use in clinical practice (46). Finally, the development
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of AI for medical applications require an integrated environment with adapted platforms and
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infrastructures with sufficient computational power. Political drive and financial support to
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develop such technological infrastructure are mandatory for a future use of AI-derived
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technologies in clinical practice. As nicely cited, medical institutions cannot be “partner to
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Industry 4.0 if it is still stuck in Bureaucracy 1.0” (13). The role of medical practitioners and
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surgeons is of utmost importance in the AI revolution. While institutional and technical
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support is undoubtedly required, medical practitioners should play a role to develop
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multicenter clinical data registries. A close collaboration between doctors and data scientists
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is a key-point to develop fitting tools and guide engineers to answer the right medical
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question with the right data (26). The expertise of medical practitioners and surgeons remain
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necessary to evaluate the relevance and the safety of AI applications for clinical practice.
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Conclusion
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AI offers many perspectives for medical applications in vascular surgery including the
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management and analysis of medical data, the development of expert systems for prediction
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and decision making or the evolution of devices. It could be used in several settings including
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the patients’ care, education and training of surgeons, healthcare information and surveillance
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system, research or improvement of evidence-based medicine. While AI is full of promising,
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there remains many challenges to face and unknowns to discover under the surface of the
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“algorithmic iceberg”. The involvement of surgeons and medical professionals in these
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technological changes is of utmost importance to guide and help data scientists and industrials
277
to develop relevant applications and guarantee a safe and adapted use in clinical practice.
278 279
Author contribution: All authors contributed to the conception of the manuscript, its critical
280
revision and final approval.
281 282
Conflict of interest: none to declare.
283 284
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17
Figure 1: Hierarchy of AI-related terms and relationship with medical research and clinical practice (adapted from Park et al. (47) and Krittanawong et al. (24)). AI is composed of many fields including natural language processing, robotic or vision. Expert systems and ML regroups a set of algorithms that are used to solve AI problems. AI-derived applications are being developed for both medical research and clinical practice.
S0890-5096(19)31033-7
DOI:
https://doi.org/10.1016/j.avsg.2019.11.037
Reference:
AVSG 4802
To appear in:
Annals of Vascular Surgery
Received Date: 17 October 2019 Revised Date:
17 November 2019
Accepted Date: 21 November 2019
Please cite this article as: Raffort J, Adam C, Carrier M, Lareyre F, Fundamentals in artificial intelligence for vascular surgeons, Annals of Vascular Surgery (2020), doi: https://doi.org/10.1016/ j.avsg.2019.11.037. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Elsevier Inc. All rights reserved.
Fundamentals in artificial intelligence for vascular surgeons
1 2 3
Juliette Raffort 1,2, Cédric Adam 3, Marion Carrier 3, Fabien Lareyre 2,4
4 5
1
Clinical Chemistry Laboratory, University Hospital of Nice, France
6
2
Université Côte d’Azur, CHU, Inserm U1065, C3M, Nice, France
7
3
Laboratory of Applied Mathematics and Computer Science (MICS), CentraleSupélec,
8
Université Paris-Saclay, France
9
4
Department of Vascular Surgery, University Hospital of Nice, France
10 11 12
Correspondence to be addressed to:
13
Juliette Raffort, MD, PhD
14
Department of Clinical Biochemistry, University Hospital of Nice, France
15
30 Avenue de la Voie Romaine, 06001 Nice, France
16
Telephone number: +33 (0)4 92 03 87 90
17
Email address: [email protected]
18 19
Keywords: artificial intelligence, machine learning, deep learning, big data
20 21 22 23 24 25
1
26
Abstract
27
Artificial intelligence (AI) corresponds to a broad discipline that aims to design systems
28
which display properties of human intelligence. While it has led to many advances and
29
applications in daily life, its introduction in medicine is still in its infancy. AI has created
30
interesting perspectives for medical research and clinical practice but has been sometimes
31
associated with hype leading to a misunderstanding of its real capabilities. Here, we aim to
32
introduce the fundamentals notions of AI and to bring an overview of its potential
33
applications for medical and surgical practice. In the limelight of current knowledge, limits
34
and challenges to face as well as future directions are discussed.
35 36 37
Introduction
38
Since its first introduction at the Darmouth College conference in 1956, significant advances
39
in artificial intelligence (AI) have been achieved leading to applications in daily life in various
40
fields from industry to finance, education, transport, marketing, media, telecommunications or
41
computer science. The earliest work of AI in medicine arose in the 1970’s and with the
42
development of machine learning techniques, AI has raised promises and hopes for
43
applications in clinical practice. On the downside, AI has created hype and ideas derived from
44
fantasy and imaginary world, sometimes leading to a misuse or a misunderstanding of the real
45
capabilities of AI systems for healthcare. In this report, we aim to introduce the fundamental
46
notions regrouped under the generic term “AI” and focus on the main techniques used for
47
medical applications. Potential applications for medical and surgical practice are discussed,
48
along with the current limits and future directions.
49 50
Fundamentals of AI in medicine
2
51
Terminology
52
AI corresponds to a broad discipline that aims to understand and design systems that display
53
properties of human intelligence, such as reasoning, learning, adaptation, interaction or
54
sensory understanding (1). It is an inter-disciplinary approach using principles and devices in
55
a variety of fields such as computation, mathematics, logic, mechanics, or biology to
56
understand, model or replicate intelligence and cognitive processes (2). In practical terms, AI
57
can be considered as the ability of a machine or a device to make autonomous decision based
58
on data collected (3). The terms related to AI and their hierarchy are summarized in Figure 1.
59
AI includes various branches such as robotics (applications of AI for motion), vision
60
(applications for videos or images) or natural language processing (applications of AI for
61
speech or written language).
62
Machine learning (ML) is a sub-discipline of AI where a technique ifs used to give the
63
machine the ability to learn (3). ML regroups a set of algorithms that are used to solve AI
64
problems (Figure 1). The technique is able to identify patterns from large datasets without
65
being explicitly programmed and without any a priori assumption. Input and output data are
66
provided and the machine determines the process by which the given input produces the given
67
output data. The process can then predict the unknown output when new input data is
68
provided (4). Three main ways for a machine to learn can be distinguished including
69
supervised, unsupervised and reinforcement learning.
70
In supervised learning, labelled inputs and outputs are provided for training. The program is
71
trained to learn a function that maps an input to an output of interest in a dataset that has been
72
labeled by a human supervisor. Once the associations have been identified based on existing
73
data and the inferred function created, it can be used to predict and map new data (5, 6). The
74
algorithm generalizes from the training data to unseen and new situations. This method is
3
75
usually used to predict or classify future events, or to find which variables are most relevant
76
to the outcomes (7).
77
On the opposite, unsupervised learning does not require any labelled or annotated data. It
78
helps to find previously unknown patterns in datasets without pre-existing labels instead of
79
trying to fit an input to an output (3). The aim is to uncover hidden patterns and natural
80
structure in dataset (8). It allows to explore complex relationships among variables in datasets
81
without any a priori assumptions.
82
Finally, reinforcement learning trains computer programs to make decisions and take actions
83
based on their ability to maximize a defined reward (5). It does not require labelled data. The
84
aim of the program is to collect as much reward as possible, finding a balance between
85
exploration and exploitation of current knowledge. The program chooses any action as a
86
function of the history and reward. This approach has been inspired from behavioral
87
psychology such as the operant conditioning and is useful to develop automated predictions or
88
actions.
89
Deep learning (DL) is a specific type of ML method which allows a machine to be fed by a
90
large quantity of raw data and to discover the representations necessary for detection and
91
classification (5). DL is a class of ML algorithms that uses multiple layers to progressively
92
extract higher level features from the raw input. It regroups computational models that are
93
composed of multiple processing layers to transform the data and amplify the aspects of the
94
input which are important for discrimination and suppress irrelevant variations (5, 9). The
95
development of DL algorithms has led to many advances in various fields including speech
96
recognition, visual object recognition and detection, drug discovery or genomics (10).
97
Numerous DL algorithms exist, among which Artificial Neural Network (ANN) and
98
Convolutional Neural Network (CNN) are the most commonly used for medical applications.
4
99
ANN are inspired by biological nervous system in which neurons transmit electrical signals
100
from one end to the other. Each neuron is connected to multiple other neurons, organized in
101
layers and receive input data, process it to give an output (11) The first layer is called the
102
“input layer” and the last layer the “output layer”. The “hidden layers” between are often
103
called “black box” as it is unknown how the neural network derived a specific result. During
104
the learning process, the connection strength between the neurons is adapted by the
105
applications of weights which weaken or enhance the signal. The updating of the weights is
106
determined using the back-propagation method of the error estimated between the predicted
107
outcome and the correct output.
108
Convolutional neural network (CNN) is often applied to image processing and enables
109
detection, segmentation and recognition of objects and regions within images (10). It is
110
composed of a multilayered network with mathematical model simulating some of the
111
properties of the visual cortex. The neurons process portions of the input image and provide a
112
filtered representation of the original image. The process is repeated in each layer until the
113
final input.
114 115 116
Overview of AI in Medicine
117
Since the earliest work on AI in medicine in the 1970’s (12), advances of technology and
118
computational methods has led to a growing interest for potential applications in both medical
119
research and clinical practice (Figure 1). According to Vuong et al., the use of AI in medicine
120
can schematically be categorized in to two main branches: the physical branch which includes
121
the development of assistive robots for care, surgery or drug delivery, and the virtual branch
122
including the development of informatics approach and expert systems (13).
5
123
The number of publications of AI in medicine has increased exponentially over the past
124
decade (14, 15) . The total number of published paper on the topic in the Web of Science
125
(WOS) database was estimated to be 27 451 at the end of the year 2018, with 61% of the
126
articles published between 2014 and 2018 (14). The most prolific countries in AI in health
127
and medicine were the United States, accounting for approximately of 30.8% of the published
128
articles, followed by China, European countries and India (14). France was ranked at the 6th
129
place with a total number of publication of 1308 (3.8%) at the end of 2018. Interestingly,
130
trends have been observed regarding the topic studied and the potential applications of AI in
131
medicine (13). Western countries tend to have a clinically-focus approach with projects
132
oriented toward specific diseases such as cancer or cardiovascular diseases. Low and middle-
133
income countries rather develop applications oriented toward the public healthcare sector
134
(13).
135
There is a growing interest of applications of AI in medicine involving all medical and
136
surgical specialties. While there is a large number of publications on the topic related to
137
cancerology, radiology, cardiology or even neurology, AI is less reported in vascular disease
138
management (15). Nevertheless, the increase of AI-related publications for vascular disease
139
has been estimated to be 147% over a 17-year evaluation period (2000- 2016), suggesting a
140
real potential for a future use in medical practice.
141
Concomitantly with the raise of published articles, the number of registered clinical trials has
142
drastically increased over the past few years, demonstrating the renewed interest toward
143
applications of AI for clinical practice (16). Finally, the venture capital backed for healthcare
144
AI startups has raised among the past few years, suggesting that AI-driven products and
145
technologies are to be expected in the next future (17).
146
Applications of AI in cardiovascular diseases and vascular surgery
147
Type of medical data
6
148
A wide range of medical data can be used to develop AI algorithms and the term “big data”
149
corresponds to a large amount of collected data. Electronic health records have been
150
extensively used as they contain valuable information including structured data (such as
151
diagnosis, treatments or laboratory tests) and unstructured data (such as free-text clinical
152
notes) (9, 18). While health electronic reports present the advantage to be easily available,
153
many challenges remain for developing AI algorithms. Data are often heterogeneous and
154
sparse, with missing information. Besides, the coding systems used in medical records vary
155
between institutions all over the world (18). In addition to health records, medical images are
156
a major source of data useful to assess the diagnosis, the prognosis and the therapeutic
157
response of patients. ML and DL approaches have been widely developed in cardiovascular
158
imaging to facilitate image segmentation and classification (3, 7, 19). Physiological data (e.g.
159
electrocardiograms) can also be analyzed and several AI methods have been applied to help in
160
the interpretation (20). Cardiovascular diseases are impacted by environmental conditions and
161
behavioral data can be used to assess the link between the lifestyle and health of patients. This
162
can include video and conversational data, social media or mobile sensor data (18). At last, AI
163
can be applied in medical research settings for the analysis and the discovery of patterns in
164
huge amounts of biological data such as genomic, transcriptomic or proteomic (9). Published
165
data from previous work are also a major source of information in both fundamental and
166
clinical research. AI approaches can be used to extract useful information from biomedical
167
literature data (21). Natural language processing (NLP) is a subfield of AI which enable a
168
computer program to understand human language. Such systems are useful to automatically
169
identify keywords and relevant information in large medical database. Finally, AI approaches
170
have also been developed to facilitate clinical trials design and data mining (22, 23). They
171
may be useful to optimize the power of clinical trials and facilitate the discrimination of
172
inclusion and exclusion criteria.
7
173 174
Technical consideration for deep learning
175
While ML initially required huge computational power, the development of Graphical
176
Processing Unit (GPU) has allowed DL to be performed on desktop machines (6). The
177
selection of an appropriate software package and the data preparation is a mandatory step.
178
This involves feature selection and treatment of missing data. The raw data are transformed
179
into features that are clinically relevant to reduce dimensionality of the data. Feature selection
180
is performed by the algorithm and allows to treat a large volume of complex data (24).
181
Missing data can potentially affect the performances to learn and their identification is crucial
182
to limit their impact by inserting labels or using auto-encoders (24).
183
Numerous DL algorithms exist and the choice of the most appropriate algorithm depends on
184
the aim of the study and dataset available. Once the algorithm has been defined, a large
185
amount of data is necessary during the training of the model to minimize bias due to over-
186
fitting (6). Indeed, when a dataset is imbalanced, predictions are biased towards the majority
187
samples. One method used to reduce the over-fitting problem and improve classification
188
accuracy is to augment the data. A key point in evaluating an AI algorithm accuracy is
189
external validation, which consists in testing the algorithm when new data are fed into the
190
model (24). AI technologies enable to search for patterns in given data without any
191
assumptions and understanding of concepts and principles, which make them strongly
192
dependent on the training data. Data used for validation should be collected independently
193
from the training dataset to assess the generalizability of the model (25).
194 195
Applications of AI in vascular surgery
196
Several applications derived from AI are to be expected in the healthcare information system
197
and the management of clinical data registries. Such approaches may in term allow the
8
198
analysis and the identification of patterns in big data including clinical, biological and
199
imaging results (26). Surgeons play a central role in clinical data registries and a close
200
collaboration with data scientists would allow to optimize the relevance and the efficiency of
201
heath information programs and medical registries. Such advances could lead to various
202
perspectives in public healthcare surveillance as well as in medical research.
203
In addition to management of medical data, AI techniques are currently being developed
204
toward helping in the diagnosis and clinical decision making. In vascular surgery, imaging is
205
a key step in the care provided to patients, allowing to confirm the diagnosis, evaluate the
206
prognosis and plan the surgical intervention. Vascular segmentation is challenging as vessels
207
exhibit high variability of morphology, size and curvature. AI techniques can help to improve
208
image segmentation, pattern recognition and could be useful to automatically perform
209
repetitive tasks, improving the reproducibility and reducing the computational time. As an
210
example, several AI-derived methods have been applied to improve aortic aneurysm
211
segmentation allowing a deep characterization of the aneurysm geometry and morphology
212
(27-31). ML was also used to develop fully automated pipelines to detect and measure
213
vascular calcifications from CT-scan images (32, 33).
214
Computational programs were developed for image segmentation and risk stratification in
215
patients with carotid artery stenosis (34-36). AI offers interesting perspectives for image
216
segmentation, automation, analysis of data from health medical records, facilitating and
217
improving data acquisition and quantitative measurements in a large dataset of patients. A
218
combination of these methods may help to better evaluate the risk of patients and the post-
219
operative outcomes. As an example, several DL algorithms have been developed to evaluate
220
the risk of aortic aneurysm growth and rupture or to predict the outcomes after aneurysm
221
surgical repair (37-41) (42). There is currently no clear consensus on how to manage complex
222
aortic aneurysms such as juxta-renal AAA. Recent advances in endovascular technology have
9
223
led to the availability of multiple devices including fenestrated or chimney-endografts. A
224
recent study highlighted significant variations in the management of juxta-renal AAA where
225
either continue surveillance, endovascular or open repair were preconized by vascular
226
surgeons for the same patient depending on the country (43). This underlines a real need to
227
develop new tools to help surgeons to better evaluate the most adapted therapeutic approach.
228
By enabling the development of multi-variable scores combining clinical, biological and
229
imaging characteristics, AI may allow to classify the patient status, better assess the risk of
230
per and post-operative complications and guide surgeons to choose the most appropriate
231
surgical technique, especially in case of complex aneurysm. AI-derived methods could help to
232
improve the evaluation of the risks and prognosis of patients, facilitate the pre-operative
233
planning and propose a more personalized therapeutic approach. Finally, AI could also be
234
applied for medical training and education by enabling simulation of clinical situations. As an
235
example, virtual reality simulators have been developed and could be used for the training of
236
young surgeons on the basic endovascular procedures (44, 45).
237 238
Current limits and future directions
239
While AI-derived technologies offer potential interesting applications for medical research
240
and clinical practice, several challenges remain. The first technical concern relates to the data.
241
DL requires the availability of huge amount of data to train an effective and robust model.
242
Medical data are very heterogeneous, can be incomplete or inaccurate. They can also evolve
243
over time and be hardly interpretable (9). In addition, data are generated by various
244
manufacturers, are stored in many distinct registries, leading to a wide heterogeneity of
245
quality, formats, resolutions, dimensions and scales. A huge effort of standardization is
246
necessary to develop large multicenter databases. With the increase number of AI in health
247
models, the privacy and the security of data is another challenge to face for data sharing (18).
10
248
Another concern regarding AI algorithms is their external validation to evaluate the
249
generalizability of the results. Ideally, multicenter registries should be used to take into
250
account a broad spectrum of patient’s demographics and the replication of the results by other
251
groups should be evaluated. The interpretability of AI algorithms can also be challenging as
252
techniques such as ANN are based on a “black box” design, with little ability to identify how
253
and why such patterns were identified by the computer (26). The algorithms do not give any
254
causal relationship. The accountability of algorithms and the verifiability of analyses
255
generated is a major challenge for their use in clinical practice (46). Finally, the development
256
of AI for medical applications require an integrated environment with adapted platforms and
257
infrastructures with sufficient computational power. Political drive and financial support to
258
develop such technological infrastructure are mandatory for a future use of AI-derived
259
technologies in clinical practice. As nicely cited, medical institutions cannot be “partner to
260
Industry 4.0 if it is still stuck in Bureaucracy 1.0” (13). The role of medical practitioners and
261
surgeons is of utmost importance in the AI revolution. While institutional and technical
262
support is undoubtedly required, medical practitioners should play a role to develop
263
multicenter clinical data registries. A close collaboration between doctors and data scientists
264
is a key-point to develop fitting tools and guide engineers to answer the right medical
265
question with the right data (26). The expertise of medical practitioners and surgeons remain
266
necessary to evaluate the relevance and the safety of AI applications for clinical practice.
267 268
Conclusion
269
AI offers many perspectives for medical applications in vascular surgery including the
270
management and analysis of medical data, the development of expert systems for prediction
271
and decision making or the evolution of devices. It could be used in several settings including
272
the patients’ care, education and training of surgeons, healthcare information and surveillance
11
273
system, research or improvement of evidence-based medicine. While AI is full of promising,
274
there remains many challenges to face and unknowns to discover under the surface of the
275
“algorithmic iceberg”. The involvement of surgeons and medical professionals in these
276
technological changes is of utmost importance to guide and help data scientists and industrials
277
to develop relevant applications and guarantee a safe and adapted use in clinical practice.
278 279
Author contribution: All authors contributed to the conception of the manuscript, its critical
280
revision and final approval.
281 282
Conflict of interest: none to declare.
283 284
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Figure 1: Hierarchy of AI-related terms and relationship with medical research and clinical practice (adapted from Park et al. (47) and Krittanawong et al. (24)). AI is composed of many fields including natural language processing, robotic or vision. Expert systems and ML regroups a set of algorithms that are used to solve AI problems. AI-derived applications are being developed for both medical research and clinical practice.