scholarly journals AI in spotting high-risk characteristics of medical imaging and molecular pathology

2021 ◽  
Author(s):  
Chong Zhang ◽  
Jionghui Gu ◽  
Yangyang Zhu ◽  
Zheling Meng ◽  
Tong Tong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Clinical Decision ◽  
Disease Diagnosis ◽  
Imaging Analysis ◽  
Imaging Characteristics ◽  
Risk Characteristics ◽  
Depth Analysis ◽  
Artificial Intelligence Technology ◽  
Rich Information

Abstract Medical imaging provides a comprehensive perspective and rich information for disease diagnosis. Combined with artificial intelligence technology, medical imaging can be further mined for detailed pathological information. Many studies have shown that the macroscopic imaging characteristics of tumors are closely related to microscopic gene, protein and molecular changes. In order to explore the function of artificial intelligence algorithms in in-depth analysis of medical imaging information, this paper reviews the articles published in recent years from three perspectives: medical imaging analysis method, clinical applications and the development of medical imaging in the direction of pathological molecular prediction. We believe that AI-aided medical imaging analysis will be extensively contributing to precise and efficient clinical decision.

Reform and innovation of artificial intelligence technology for information service in university physical education

2020 ◽  
pp. 1-11
Author(s):  
Jianye Zhang
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Physical Education ◽  
Information Service ◽  
Management Decision ◽  
Negative Effects ◽  
Education Management ◽  
Depth Analysis ◽  
Artificial Intelligence Technology ◽  
The Difference

This article analyzes the reform of information services in university physical education based on artificial intelligence technology and conducts in-depth and innovative research on it. In-depth analysis of the relationship between big data and the development and application of information technology such as the Internet, Internet of Things, cloud computing, to clarify the difference and connection between big data, informatization and intelligence. Artificial intelligence will bring opportunities for changes in data collection, management decision-making, governance models, education and teaching, scientific research services, evaluation and evaluation of physical education in our university. At the same time, big data education management in colleges and universities faces many challenges such as the balance of privacy and freedom, data hegemony, data junk, data standards, and data security, and they have many negative effects. In accordance with the requirements of educational modernization, centering on the goal of intelligent and humanized education management, it aims existing issues in college physical education management.

Recent Advances in Medical Image Processing

2020 ◽  
pp. 1-14
Author(s):  
Zhen Huang ◽  
Qiang Li ◽  
Ju Lu ◽  
Junlin Feng ◽  
Jiajia Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Artificial Intelligence Technology ◽  
Deep Convolution Neural Network

<b><i>Background:</i></b> Application and development of the artificial intelligence technology have generated a profound impact in the field of medical imaging. It helps medical personnel to make an early and more accurate diagnosis. Recently, the deep convolution neural network is emerging as a principal machine learning method in computer vision and has received significant attention in medical imaging. <b><i>Key Message:</i></b> In this paper, we will review recent advances in artificial intelligence, machine learning, and deep convolution neural network, focusing on their applications in medical image processing. To illustrate with a concrete example, we discuss in detail the architecture of a convolution neural network through visualization to help understand its internal working mechanism. <b><i>Summary:</i></b> This review discusses several open questions, current trends, and critical challenges faced by medical image processing and artificial intelligence technology.

scholarly journals Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations

2021 ◽  
Author(s):  
Laleh Seyyed-Kalantari ◽  
Haoran Zhang ◽  
Matthew B. A. McDermott ◽  
Irene Y. Chen ◽  
Marzyeh Ghassemi
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Low Socioeconomic Status ◽  
Disease Diagnosis ◽  
Low Socioeconomic ◽  
X Ray ◽  
Female Patients ◽  
Pathology Classification ◽  
Black Patients ◽  
Chest X Ray

AbstractArtificial intelligence (AI) systems have increasingly achieved expert-level performance in medical imaging applications. However, there is growing concern that such AI systems may reflect and amplify human bias, and reduce the quality of their performance in historically under-served populations such as female patients, Black patients, or patients of low socioeconomic status. Such biases are especially troubling in the context of underdiagnosis, whereby the AI algorithm would inaccurately label an individual with a disease as healthy, potentially delaying access to care. Here, we examine algorithmic underdiagnosis in chest X-ray pathology classification across three large chest X-ray datasets, as well as one multi-source dataset. We find that classifiers produced using state-of-the-art computer vision techniques consistently and selectively underdiagnosed under-served patient populations and that the underdiagnosis rate was higher for intersectional under-served subpopulations, for example, Hispanic female patients. Deployment of AI systems using medical imaging for disease diagnosis with such biases risks exacerbation of existing care biases and can potentially lead to unequal access to medical treatment, thereby raising ethical concerns for the use of these models in the clinic.

scholarly journals Global review of assisted diagnosis tools using medical database and artificial intelligence methods to improve complex disease diagnosis

2021 ◽  
Author(s):  
Anne Blériot ◽  
Franck Le Meur ◽  
Guillaume De Chamisso
Keyword(s):  
Artificial Intelligence ◽  
Rare Disease ◽  
Complex Disease ◽  
Disease Diagnosis ◽  
Advisory Board ◽  
Screening Tools ◽  
Medical Database ◽  
Depth Analysis ◽  
Patient Representatives ◽  
Assisted Diagnosis

Abstract Millions of people worldwide suffer from a rare disease. Many among them without a definitive diagnosis. The object of this study was to identify, evaluate and rate the most appropriate and promising technological tools to help patients effectively navigate their rare disease journey and reduce their exposure to diagnostic wandering.For the analysis of available tools, products were separated into four technology categories: artificial intelligence, assisted anamnesis (symptom checkers), awareness/patient self-screening, direct identification of patients via screening. Tools were then ranked according to two criteria: impact on patients and operability and subsequently narrowed down further for more in-depth analysis. In two separate advisory board meetings, the most promising tools were then evaluated by healthcare professionals and patient representatives, respectively.Across four categories and 107 different tools and means for the purposes of reducing diagnostic wandering in rare disease patients, instruments such as Symptoma, Isabel or FindZebra emerged as the favored solution on both advisory board meeting groups. Symptoma was selected to be further evaluated in a comprehensive pilot study with cardiologists and general practitioners in a real-world setting.

scholarly journals A Promising and Challenging Approach: Radiologists’ Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1924
Author(s):  
Tianming Wang ◽  
Zhu Chen ◽  
Quanliang Shang ◽  
Cong Ma ◽  
Xiangyu Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Treatment Decision ◽  
Disease Diagnosis ◽  
Future Research ◽  
X Rays ◽  
Imaging Data ◽  
Efficacy Evaluation ◽  
Treatment Decision Making ◽  
Diagnosis And Classification

Chest X-rays (CXR) and computed tomography (CT) are the main medical imaging modalities used against the increased worldwide spread of the 2019 coronavirus disease (COVID-19) epidemic. Machine learning (ML) and artificial intelligence (AI) technology, based on medical imaging fully extracting and utilizing the hidden information in massive medical imaging data, have been used in COVID-19 research of disease diagnosis and classification, treatment decision-making, efficacy evaluation, and prognosis prediction. This review article describes the extensive research of medical image-based ML and AI methods in preventing and controlling COVID-19, and summarizes their characteristics, differences, and significance in terms of application direction, image collection, and algorithm improvement, from the perspective of radiologists. The limitations and challenges faced by these systems and technologies, such as generalization and robustness, are discussed to indicate future research directions.

scholarly journals Using artificial intelligence on dermatology conditions in Uganda: A case for diversity in training data sets for machine learning

2019 ◽  
Author(s):  
Louis Henry Kamulegeya ◽  
Mark Okello ◽  
John Mark Bwanika ◽  
Davis Musinguzi ◽  
William Lubega ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diagnostic Accuracy ◽  
Body Part ◽  
Clinical Decision ◽  
Disease Diagnosis ◽  
Training Data ◽  
Correct Prediction ◽  
Data Sets ◽  
Body Parts ◽  
Correct Treatment

AbstractIntroductionArtificial intelligence (AI) in healthcare has gained momentum with advances in affordable technology that has potential to help in diagnostics, predictive healthcare and personalized medicine. In pursuit of applying universal non-biased AI in healthcare, it is essential that data from different settings (gender, age and ethnicity) is represented. We present findings from beta-testing an AI-powered dermatological algorithm called Skin Image Search, by online dermatology company First Derm on Fitzpatrick 6 skin type (dark skin) dermatological conditions.Methods123 dermatological images selected from a total of 173 images retrospectively extracted from the electronic database of a Ugandan telehealth company, The Medical Concierge Group (TMCG) after getting their consent. Details of age, gender and dermatological clinical diagnosis were analyzed using R on R studio software to assess the diagnostic accuracy of the AI app along disease diagnosis and body part. Predictability levels of the AI app was graded on a scale of 0 to 5, where 0-no prediction made and 1-5 demonstrating reducing correct prediction.Results76 (62%) of the dermatological images were from females and 47 (38%) from males. The 5 most reported body parts were; genitals (20%), trunk (20%), lower limb (14.6%), face (12%) and upper limb (12%) with the AI app predicting a diagnosis in 62% of image body parts uploaded. Overall diagnostic accuracy of the AI app was low at 17% (21 out of 123 predictable images) with varying predictability levels correctness i.e. 1-8.9%, 2-2.4%, 3-2.4%, 4-1.6%, 5-1.6% with performance along individual diagnosis highest with dermatitis (80%).ConclusionThere is a need for diversity in the image datasets used when training dermatology algorithms for AI applications in clinical decision support as a means to increase accuracy and thus offer correct treatment across skin types and geographies.

scholarly journals Clinical Utility and Functionality of an Artificial Intelligence–Based App to Predict Mortality in COVID-19: Mixed Methods Analysis (Preprint)

2021 ◽  
Author(s):  
Ahmed Abdulaal ◽  
Aatish Patel ◽  
Ahmed Al-Hindawi ◽  
Esmita Charani ◽  
Saleh A Alqahtani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Main Concern ◽  
Classification Problems ◽  
End User ◽  
System Usability Scale ◽  
System Usability ◽  
Artificial Intelligence Technology

BACKGROUND The artificial neural network (ANN) is an increasingly important tool in the context of solving complex medical classification problems. However, one of the principal challenges in leveraging artificial intelligence technology in the health care setting has been the relative inability to translate models into clinician workflow. OBJECTIVE Here we demonstrate the development of a COVID-19 outcome prediction app that utilizes an ANN and assesses its usability in the clinical setting. METHODS Usability assessment was conducted using the app, followed by a semistructured end-user interview. Usability was specified by effectiveness, efficiency, and satisfaction measures. These data were reported with descriptive statistics. The end-user interview data were analyzed using the thematic framework method, which allowed for the development of themes from the interview narratives. In total, 31 National Health Service physicians at a West London teaching hospital, including foundation physicians, senior house officers, registrars, and consultants, were included in this study. RESULTS All participants were able to complete the assessment, with a mean time to complete separate patient vignettes of 59.35 (SD 10.35) seconds. The mean system usability scale score was 91.94 (SD 8.54), which corresponds to a qualitative rating of “excellent.” The clinicians found the app intuitive and easy to use, with the majority describing its predictions as a useful adjunct to their clinical practice. The main concern was related to the use of the app in isolation rather than in conjunction with other clinical parameters. However, most clinicians speculated that the app could positively reinforce or validate their clinical decision-making. CONCLUSIONS Translating artificial intelligence technologies into the clinical setting remains an important but challenging task. We demonstrate the effectiveness, efficiency, and system usability of a web-based app designed to predict the outcomes of patients with COVID-19 from an ANN.

scholarly journals Articles That Use Artificial Intelligence for Ultrasound: A Reader’s Guide

2021 ◽  
Vol 11 ◽  
Author(s):  
Ming Kuang ◽  
Hang-Tong Hu ◽  
Wei Li ◽  
Shu-Ling Chen ◽  
Xiao-Zhou Lu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Clinical Decision ◽  
Disease Diagnosis ◽  
Lesion Detection ◽  
Machine Learning Algorithms ◽  
Diagnosis And Prognosis ◽  
Target Segmentation ◽  
Time Scanning

Artificial intelligence (AI) transforms medical images into high-throughput mineable data. Machine learning algorithms, which can be designed for modeling for lesion detection, target segmentation, disease diagnosis, and prognosis prediction, have markedly promoted precision medicine for clinical decision support. There has been a dramatic increase in the number of articles, including articles on ultrasound with AI, published in only a few years. Given the unique properties of ultrasound that differentiate it from other imaging modalities, including real-time scanning, operator-dependence, and multi-modality, readers should pay additional attention to assessing studies that rely on ultrasound AI. This review offers the readers a targeted guide covering critical points that can be used to identify strong and underpowered ultrasound AI studies.

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