Artificial Intelligence Program Provides Rapid, Accurate Diagnosis of Dystonia

2020 ◽  
Vol 20 (20) ◽  
pp. 12-13
Author(s):  
Dan Hurley
Keyword(s):  
Artificial Intelligence ◽  
Accurate Diagnosis ◽  
Artificial Intelligence Program

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.

scholarly journals Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology

2019 ◽  
Vol 18 ◽  
pp. 153601211986907 ◽  
Author(s):  
Ian R. Duffy ◽  
Amanda J. Boyle ◽  
Neil Vasdev
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Pet Imaging ◽  
Accurate Diagnosis ◽  
Emission Tomography ◽  
Treatment Regimens ◽  
Positron Emission ◽  
Digital Biomarkers

Machine learning (ML) algorithms have found increasing utility in the medical imaging field and numerous applications in the analysis of digital biomarkers within positron emission tomography (PET) imaging have emerged. Interest in the use of artificial intelligence in PET imaging for the study of neurodegenerative diseases and oncology stems from the potential for such techniques to streamline decision support for physicians providing early and accurate diagnosis and allowing personalized treatment regimens. In this review, the use of ML to improve PET image acquisition and reconstruction is presented, along with an overview of its applications in the analysis of PET images for the study of Alzheimer's disease and oncology.

scholarly journals Initial User Experience With an Artificial Intelligence Program for the Preliminary Design of Centrifugal Compressors

1987 ◽  
Author(s):  
Carol J. Russo ◽  
Dennis J. Nicklaus ◽  
Siu S. Tong
Keyword(s):  
Artificial Intelligence ◽  
Design Tool ◽  
Parameter Study ◽  
Balance Performance ◽  
New Approach ◽  
Trade Offs ◽  
Component Design ◽  
Small Set ◽  
Development Risks ◽  
Artificial Intelligence Program

A new approach is evaluated for the design of turbomachinery components using existing analysis codes coupled to a generic Artificial Intelligence (AI) software framework called ENGINEOUS. This AI framework uses intelligent search techniques with a small set of basic component design rules to iterate to an optimized solution and to quantify parameter trade-offs. Initial experience with ENGINEOUS indicates that it is a powerful design tool which quickly identifies non-obvious solutions balanced for conflicting multiple goals in a small number of iterations which vary linearly with the number of variables. The solution path and driving logic are easily visible to the designer and a parameter study option can rapidly quantify potential design trade-offs which together allow a critique of the selected design to balance performance against development risks. Because this AI design approach fosters intelligent interface with the designer and is generic, the potential application areas and productivity benefits appear enormous.

An Artificial Intelligence program to advise physicians regarding antimicrobial therapy

1973 ◽  
Vol 6 (6) ◽  
pp. 544-560 ◽  
Author(s):  
Edward H. Shortliffe ◽  
Stanton G. Axline ◽  
Bruce G. Buchanan ◽  
Thomas C. Merigan ◽  
Stanley N. Cohen
Keyword(s):  
Artificial Intelligence ◽  
Antimicrobial Therapy ◽  
Artificial Intelligence Program

scholarly journals Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Zubair Ahmad ◽  
Shabina Rahim ◽  
Maha Zubair ◽  
Jamshid Abdul-Ghafar
Keyword(s):  
Artificial Intelligence ◽  
Clinical Information ◽  
Accurate Diagnosis ◽  
Surgical Pathology ◽  
Computer Algorithms ◽  
Comprehensive Review ◽  
Future Role ◽  
Personalized Care ◽  
High Level

Abstract Background The role of Artificial intelligence (AI) which is defined as the ability of computers to perform tasks that normally require human intelligence is constantly expanding. Medicine was slow to embrace AI. However, the role of AI in medicine is rapidly expanding and promises to revolutionize patient care in the coming years. In addition, it has the ability to democratize high level medical care and make it accessible to all parts of the world. Main text Among specialties of medicine, some like radiology were relatively quick to adopt AI whereas others especially pathology (and surgical pathology in particular) are only just beginning to utilize AI. AI promises to play a major role in accurate diagnosis, prognosis and treatment of cancers. In this paper, the general principles of AI are defined first followed by a detailed discussion of its current role in medicine. In the second half of this comprehensive review, the current and future role of AI in surgical pathology is discussed in detail including an account of the practical difficulties involved and the fear of pathologists of being replaced by computer algorithms. A number of recent studies which demonstrate the usefulness of AI in the practice of surgical pathology are highlighted. Conclusion AI has the potential to transform the practice of surgical pathology by ensuring rapid and accurate results and enabling pathologists to focus on higher level diagnostic and consultative tasks such as integrating molecular, morphologic and clinical information to make accurate diagnosis in difficult cases, determine prognosis objectively and in this way contribute to personalized care.

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