Challenges of developing artificial intelligence‐assisted tools for clinical medicine

2021 ◽  
Vol 36 (2) ◽  
pp. 295-298
Author(s):  
Dennis L Shung ◽  
Joseph J Y Sung
Keyword(s):  
Artificial Intelligence ◽  
Clinical Medicine

Top 100 most-cited articles on medical imaging applications of artificial intelligence: a bibliometric analysis (Preprint)

2020 ◽  
Author(s):  
Ying Liu ◽  
Ziyan Yu ◽  
Shuolan Jing ◽  
Honghu Jiang ◽  
Chunxia Wang
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Bibliometric Analysis ◽  
Clinical Medicine ◽  
Medical Physics ◽  
Medical Image Analysis ◽  
The United States ◽  
Independent Learning ◽  
Learning Ability ◽  
Core Technology

BACKGROUND Artificial intelligence (AI) has penetrated into almost every aspect of our lives and is rapidly changing our way of life. Recently, the new generation of AI taking machine learning and particularly deep convolutional neural network theories as the core technology, has stronger learning ability and independent learning evolution ability, combined with a large amount of learning data, breaks through the bottleneck limit of model accuracy, and makes the model efficient use. OBJECTIVE To identify the 100 most cited papers in artificial intelligence in medical imaging, we performed a comprehensive bibliometric analysis basing on the literature search on Web of Science Core Collection (WoSCC). METHODS The 100 top-cited articles published in “AI, Medical imaging” journals were identified using the Science Citation Index Database. The articles were further reviewed, and basic information was collected, including the number of citations, journals, authors, publication year, and field of study. RESULTS The highly cited articles in AI were cited between 72 and 1,554 times. The majority of them were published in three major journals: IEEE Transactions on Medical Imaging, Medical Image Analysis and Medical Physics. The publication year ranged from 2002 to 2019, with 66% published in a three-year period (2016 to 2018). Publications from the United States (56%) were the most heavily cited, followed by those from China (15%) and Netherlands (10%). Radboud University Nijmegen from Netherlands, Harvard Medical School in USA, and The Chinese University of Hong Kong in China produced the highest number of publications (n=6). Computer science (42%), clinical medicine (35%), and engineering (8%) were the most common fields of study. CONCLUSIONS Citation analysis in the field of artificial intelligence in medical imaging reveals interesting information about the topics and trends negotiated by researchers and elucidates which characteristics are required for a paper to attain a “classic” status. Clinical science articles published in highimpact specialized journals are most likely to be cited in the field of artificial intelligence in medical imaging.

scholarly journals Promise and Perils of Big Data and Artificial Intelligence in Clinical Medicine and Biomedical Research

2018 ◽  
Vol 123 (12) ◽  
pp. 1282-1284 ◽  
Author(s):  
Fatima Rodriguez ◽  
David Scheinker ◽  
Robert A. Harrington
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Biomedical Research ◽  
Clinical Medicine

scholarly journals Artificial intelligence in clinical medicine and dentistry

2017 ◽  
Vol 74 (3) ◽  
pp. 267-272 ◽  
Author(s):  
Milan Miladinovic ◽  
Branko Mihailovic ◽  
Dragan Mladenovic ◽  
Milos Duka ◽  
Dusan Zivkovic ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Medicine

nema

scholarly journals Prospects for the use of artificial intelligence systems in gastroenterology

2020 ◽  
pp. 109-117
Author(s):  
B. A. Kobrinskii ◽  
A. I. Khavkin ◽  
G. V. Volynets
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Intelligent Systems ◽  
Clinical Medicine ◽  
Correct Diagnosis ◽  
Huge Number ◽  
Powerful System ◽  
Difficult Cases ◽  
Artificial Intelligence Systems

The lecture is devoted to a new direction in clinical medicine — the possibility of using artificial intelligence — the field of computer science, which is engaged in modeling the method of acquiring and using knowledge specific to humans. The basis for a correct diagnosis is a combination of experience, the ability to think and act non-standard in difficult cases. A powerful system of generalization and classification, implemented in intelligent systems, allows you to reduce a huge number of possible situations to a small number of typical situations by which decisions or hypotheses are formed.

scholarly journals Artificial Intelligence Applications in Nutrition and Dietetics

2021 ◽  
pp. 125-127
Author(s):  
Izzet Ulker ◽  
Feride Ayyildiz
Keyword(s):  
Artificial Intelligence ◽  
Dietary Intake ◽  
Clinical Medicine ◽  
Food Preferences ◽  
Thought Processes ◽  
Learning Abilities ◽  
Assessment And Monitoring ◽  
Important Field ◽  
Nutrition And Dietetics ◽  
Artificial Intelligence Application

Artificial intelligence (AI) is a branch of computer science whose purpose is to imitate thought processes, learning abilities, and knowledge management. The increasing number of applications in experimental and clinical medicine is striking. An artificial intelligence application in the field of nutrition and dietetics is a fairly new and important field. Different apps related to nutrition are offered to the use of individuals. The importance of individual nutrition has also triggered the increase in artificial intelligence apps. It is thought that different apps such as food preferences and dietary intake can play an important role in health promotion. Researchers may have some difficulties such as remembering the frequency or amount of intake in assessment of dietary intake. Some applications used in the assessment of food consumption contribute to overcoming these difficulties. Besides, these apps facilitate the work of researchers and provide more reliable results than traditional methods. The apps to be used in the field of nutrition and dietetics should be developed by considering the disadvantages. It is thought that artificial intelligence applications will contribute to both the improvement of health and the assessment and monitoring of nutritional status.

Abstract 1122‐000235: Artificial Intelligence Algorithms for Hemorrhage Detection in CTs and MRI Scans: A Systematic Review

2021 ◽  
Vol 1 (S1) ◽  
Author(s):  
Susmita Chennareddy ◽  
Roshini Kalagara ◽  
Stavros Matsoukas ◽  
Jacopo Scaggiante ◽  
Colton Smith ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Diagnostic Accuracy ◽  
Clinical Medicine ◽  
Meta Analysis ◽  
Fine Tuning ◽  
Ct Scans ◽  
Time To Treatment ◽  
Mri Scans ◽  
3D Cnn

Introduction : Stroke is a leading cause of morbidity and mortality worldwide, with hemorrhagic strokes accounting for 10–20% of all strokes. Patients presenting with intracerebral hemorrhage (ICH) often face higher rates of mortality and poorer prognosis than those with other stroke types. As ICH treatment relies on in‐hospital neuroimaging findings, one potential barrier in the effective management of ICH includes increased time to ICH detection and treatment, particularly due to delays in imaging interpretation in busy hospitals and emergency departments. Artificial Intelligence (AI) driven software has recently been developed and become commercially available for the detection of Intracranial Hemorrhage (ICH) and Chronic Cerebral Microbleeds (CMBs). Such adjunct tools may enhance patient care by decreasing time to treatment and diagnosis by helping to adjudicate diagnoses in difficult cases. This systematic review aims to describe the current literature surrounding all currently existing AI algorithms for ICH detection with either non‐contrast computed tomography (CT) scans or CMBs detection with magnetic resonance imaging (MRI). Methods : Following PRISMA guidelines, MEDLINE and EMBASE were searched for studies published through March 1st, 2021, and all studies investigating AI algorithms for hemorrhage detection in non‐contrast CT scans or CMBs detection on MRI scans were eligible for inclusion. Any studies focusing on AI for hemorrhage segmentation only, including studies that enrolled patients with hemorrhages only as their study group, were excluded. Extracted data included development methods, training, validation and testing datasets, and accuracy metrics for each algorithm, when available. Meta‐analysis was not conducted due to heterogeneity in reported accuracy metrics and highly variant algorithmic development. The completed protocol is available for review in the PROSPERO registry. Results : After the removal of duplicates, a total of 609 studies were identified and screened. After an initial screening and full text review, 40 studies were included in this review. Of these, 18 tested a 2‐Dimensional (2D) convolutional neural network (CNN) AI algorithm, 3 used a purley 3‐Dimension (3D) CNN, and 2 utilized a hybrid 2D‐3D CNN. Of note, one software was able to identify ICH in the setting of ischemic stroke using MRI scans. Included papers noted the following challenges when developing these AI algorithms: extensive time required to create suitable datasets, the volumetric nature of the imaging exams, fine tuning the system, and focusing on the reduction of false positives. Diagnostic accuracy data was available for 21 of these studies, which reported a mean accuracy of 94.37% and a mean AUC of 0.958. Conclusions : As reported in this study, many AI‐driven software tools have been developed over the last 5 years. These tools have high diagnostic accuracy on average and have the potential to contribute to the diagnosis of ICH or CMBs with expert‐level accuracy. With time to treatment often dependent on time to diagnosis, this AI software may increase both the speed and accuracy of adjudicating diagnoses. Although there have been several obstacles faced by the developers of these algorithms, AI‐driven software is an important frontier for the future of clinical medicine.

Evolution of Clinical Medicine: From Expert Opinion to Artificial Intelligence

2020 ◽  
Vol 2 (4) ◽  
pp. 78
Author(s):  
KianoushB Kashani ◽  
Antonio Barracca ◽  
Mauro Contini ◽  
Stefano Ledda ◽  
Gianmaria Mancosu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Expert Opinion ◽  
Clinical Medicine

scholarly journals Prediction of Atrial Fibrillation Using Machine Learning: A Review

2021 ◽  
Vol 12 ◽  
Author(s):  
Andrew S. Tseng ◽  
Peter A. Noseworthy
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Atrial Fibrillation ◽  
Clinical Medicine ◽  
Cardiac Computed Tomography ◽  
Laboratory Data ◽  
Machine Learning Techniques ◽  
Imaging Data ◽  
Cerebrovascular Events ◽  
Future Direction

There has been recent immense interest in the use of machine learning techniques in the prediction and screening of atrial fibrillation, a common rhythm disorder present with significant clinical implications primarily related to the risk of ischemic cerebrovascular events and heart failure. Prior to the advent of the application of artificial intelligence in clinical medicine, previous studies have enumerated multiple clinical risk factors that can predict the development of atrial fibrillation. These clinical parameters include previous diagnoses, laboratory data (e.g., cardiac and inflammatory biomarkers, etc.), imaging data (e.g., cardiac computed tomography, cardiac magnetic resonance imaging, echocardiography, etc.), and electrophysiological data. These data are readily available in the electronic health record and can be automatically queried by artificial intelligence algorithms. With the modern computational capabilities afforded by technological advancements in computing and artificial intelligence, we present the current state of machine learning methodologies in the prediction and screening of atrial fibrillation as well as the implications and future direction of this rapidly evolving field.

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