scholarly journals A novel approach to integrating artificial intelligence into routine practice

2021 ◽  
Author(s):  
Madelyn Lew ◽  
David C. Wilbur
Keyword(s):  
Artificial Intelligence ◽  
Routine Practice ◽  
Novel Approach

The 6C model for accurately capturing the patient’s medical history

Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Taro Shimizu
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Patient Safety ◽  
Information Processing ◽  
Safety Concern ◽  
Data Gathering ◽  
Diagnostic Errors ◽  
Accurate Diagnosis ◽  
History Taking ◽  
Novel Approach

Abstract Diagnostic errors are an internationally recognized patient safety concern, and leading causes are faulty data gathering and faulty information processing. Obtaining a full and accurate history from the patient is the foundation for timely and accurate diagnosis. A key concept underlying ideal history acquisition is “history clarification,” meaning that the history is clarified to be depicted as clearly as a video, with the chronology being accurately reproduced. A novel approach is presented to improve history-taking, involving six dimensions: Courtesy, Control, Compassion, Curiosity, Clear mind, and Concentration, the ‘6 C’s’. We report a case that illustrates how the 6C approach can improve diagnosis, especially in relation to artificial intelligence tools that assist with differential diagnosis.

The performance of artificial intelligence-driven technologies in diagnosing mental disorders: An umbrella review (Preprint)

2021 ◽  
Author(s):  
Alaa Abd-Alrazaq ◽  
Jens Schneider ◽  
Dari Alhuwail ◽  
Carla T Toro ◽  
Arfan Ahmed ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Mental Disorders ◽  
Systematic Reviews ◽  
Autism Spectrum ◽  
Great Promise ◽  
Routine Practice ◽  
Daily Routine ◽  
Reference List ◽  
Umbrella Review ◽  
Compulsive Disorder

BACKGROUND Diagnosing mental disorders is usually not an easy task and requires a large amount of time and effort given the complex nature of mental disorders. Artificial intelligence (AI) has been successfully exploited in diagnosing many mental disorders. Numerous systematic reviews summarize the evidence on the accuracy of AI models in diagnosing different mental disorders. OBJECTIVE This umbrella review aims to synthesize results of previous systematic reviews on the performance of AI models in diagnosing mental disorders. METHODS To identify relevant systematic reviews, we searched 11 electronic databases, checked the reference list of the included reviews, and checked the reviews that cited the included reviews. Two reviewers independently selected the relevant reviews, extracted the data from them, and appraised their quality. We synthesized the extracted data using the narrative approach. Specifically, results of the included reviews were grouped based on the target mental disorders that the AI classifiers distinguish. RESULTS We included 15 systematic reviews of 852 citations identified by searching all databases. The included reviews assessed the performance of AI models in diagnosing Alzheimer’s disease (n=7), mild cognitive impairment (n=6), schizophrenia (n=3), bipolar disease (n=2), autism spectrum disorder (n=1), obsessive-compulsive disorder (n=1), post-traumatic stress disorder (n=1), and psychotic disorders (n=1). The performance of the AI models in diagnosing these mental disorders ranged between 21% and 100%. CONCLUSIONS AI technologies offer great promise in diagnosing mental health disorders. The reported performance metrics paint a vivid picture of a bright future for AI in this field. To expedite progress towards these technologies being incorporated into routine practice, we recommend that healthcare professionals in the field cautiously and consciously begin to explore the opportunities of AI-based tools for their daily routine. It would also be encouraging to see a greater number of meta-analyses and further systematic reviews on performance of AI models in diagnosing other common mental disorders such as depression and anxiety. CLINICALTRIAL CRD42021231558

A Comprehensive Study on Internet of Things Based on Key Artificial Intelligence Technologies and Industry 4.0

2021 ◽  
pp. 171-191
Author(s):  
Banu Çalış Uslu ◽  
Seniye Ümit Oktay Fırat
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Information Exchange ◽  
Industry 4.0 ◽  
New Technology ◽  
Smart Devices ◽  
Complex Environments ◽  
Novel Approach ◽  
Key Technologies ◽  
Shed Light

Under uncertainty, understanding and controlling complex environments is only possible with an ability to use distributed computing by the way of information exchange between devices to be able to understand the response of the system to a particular problem. From transformation of raw data in a huge distribution of network into the meaningful information, to use the understood knowledge to make rapid decisions needs to have a network composed of smart devices. Internet of things (IoT) is a novel approach, where these smart devices can communicate with each other by using key technologies of artificial intelligence (AI) in order to make timely autonomous decisions. This emerging technical advancement and realization of horizontal and vertical integration caused the fourth stage of industrialization (Industry 4.0). The objective of this chapter is to give detailed information on both IoT based on key AI technologies and Industry 4.0. It is expected to shed light on new work to be done by providing explanations about the new areas that will emerge with this new technology.

The Nature of Intelligent Analytics

2021 ◽  
pp. 1-21
Author(s):  
Zhaohao Sun ◽  
Andrew Stranieri
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Research And Development ◽  
Business Intelligence ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Novel Approach ◽  
Advanced Analytics

Intelligent analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores the nature of intelligent analytics. More specifically, this chapter identifies the foundations, cores, and applications of intelligent big data analytics based on the investigation into the state-of-the-art scholars' publications and market analysis of advanced analytics. Then it presents a workflow-based approach to big data analytics and technological foundations for intelligent big data analytics through examining intelligent big data analytics as an integration of AI and big data analytics. The chapter also presents a novel approach to extend intelligent big data analytics to intelligent analytics. The proposed approach in this chapter might facilitate research and development of intelligent analytics, big data analytics, business analytics, business intelligence, AI, and data science.

scholarly journals Artificial intelligence as a novel approach for embryo selection

2018 ◽  
Vol 110 (4) ◽  
pp. e430 ◽  
Author(s):  
A. Tran ◽  
S. Cooke ◽  
P.J. Illingworth ◽  
D.K. Gardner
Keyword(s):  
Artificial Intelligence ◽  
Embryo Selection ◽  
Novel Approach

How to improve WEEE management? Novel approach in mobile collection with application of artificial intelligence

2016 ◽  
Vol 50 ◽  
pp. 222-233 ◽  
Author(s):  
Aleksander Król ◽  
Piotr Nowakowski ◽  
Bogna Mrówczyńska
Keyword(s):  
Artificial Intelligence ◽  
Novel Approach

scholarly journals Advances in Imaging Modalities, Artificial Intelligence, and Single Cell Biomarker Analysis, and Their Applications in Cytopathology

2021 ◽  
Vol 8 ◽  
Author(s):  
Ryan P. Lau ◽  
Teresa H. Kim ◽  
Jianyu Rao
Keyword(s):  
Artificial Intelligence ◽  
Single Cell ◽  
Single Cell Analysis ◽  
High Sensitivity ◽  
Image Cytometry ◽  
Needle Aspiration ◽  
Great Promise ◽  
Routine Practice ◽  
Imaging Data ◽  
Biomarker Analysis

Several advances in recent decades in digital imaging, artificial intelligence, and multiplex modalities have improved our ability to automatically analyze and interpret imaging data. Imaging technologies such as optical coherence tomography, optical projection tomography, and quantitative phase microscopy allow analysis of tissues and cells in 3-dimensions and with subcellular granularity. Improvements in computer vision and machine learning have made algorithms more successful in automatically identifying important features to diagnose disease. Many new automated multiplex modalities such as antibody barcoding with cleavable DNA (ABCD), single cell analysis for tumor phenotyping (SCANT), fast analytical screening technique fine needle aspiration (FAST-FNA), and portable fluorescence-based image cytometry analyzer (CytoPAN) are under investigation. These have shown great promise in their ability to automatically analyze several biomarkers concurrently with high sensitivity, even in paucicellular samples, lending themselves well as tools in FNA. Not yet widely adopted for clinical use, many have successfully been applied to human samples. Once clinically validated, some of these technologies are poised to change the routine practice of cytopathology.

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