scholarly journals Application of an Artificial Intelligence Trilogy to Accelerate Processing of Suspected Patients With SARS-CoV-2 at a Smart Quarantine Station: Observational Study

10.2196/19878 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e19878
Author(s):  
Ping-Yen Liu ◽  
Yi-Shan Tsai ◽  
Po-Lin Chen ◽  
Huey-Pin Tsai ◽  
Ling-Wei Hsu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Observational Study ◽  
Processing Time ◽  
Clinical Decision Making ◽  
Image Interpretation ◽  
Clinical Decision ◽  
Tablet Computer ◽  
X Rays ◽  
Quarantine Station

Background As the COVID-19 epidemic increases in severity, the burden of quarantine stations outside emergency departments (EDs) at hospitals is increasing daily. To address the high screening workload at quarantine stations, all staff members with medical licenses are required to work shifts in these stations. Therefore, it is necessary to simplify the workflow and decision-making process for physicians and surgeons from all subspecialties. Objective The aim of this paper is to demonstrate how the National Cheng Kung University Hospital artificial intelligence (AI) trilogy of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm improves medical care and reduces quarantine processing times. Methods This observational study on the emerging COVID-19 pandemic included 643 patients. An “AI trilogy” of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm on a tablet computer was applied to shorten the quarantine survey process and reduce processing time during the COVID-19 pandemic. Results The use of the AI trilogy facilitated the processing of suspected cases of COVID-19 with or without symptoms; also, travel, occupation, contact, and clustering histories were obtained with the tablet computer device. A separate AI-mode function that could quickly recognize pulmonary infiltrates on chest x-rays was merged into the smart clinical assisting system (SCAS), and this model was subsequently trained with COVID-19 pneumonia cases from the GitHub open source data set. The detection rates for posteroanterior and anteroposterior chest x-rays were 55/59 (93%) and 5/11 (45%), respectively. The SCAS algorithm was continuously adjusted based on updates to the Taiwan Centers for Disease Control public safety guidelines for faster clinical decision making. Our ex vivo study demonstrated the efficiency of disinfecting the tablet computer surface by wiping it twice with 75% alcohol sanitizer. To further analyze the impact of the AI application in the quarantine station, we subdivided the station group into groups with or without AI. Compared with the conventional ED (n=281), the survey time at the quarantine station (n=1520) was significantly shortened; the median survey time at the ED was 153 minutes (95% CI 108.5-205.0), vs 35 minutes at the quarantine station (95% CI 24-56; P<.001). Furthermore, the use of the AI application in the quarantine station reduced the survey time in the quarantine station; the median survey time without AI was 101 minutes (95% CI 40-153), vs 34 minutes (95% CI 24-53) with AI in the quarantine station (P<.001). Conclusions The AI trilogy improved our medical care workflow by shortening the quarantine survey process and reducing the processing time, which is especially important during an emerging infectious disease epidemic.

scholarly journals Application of an Artificial Intelligence Trilogy to Accelerate Processing of Suspected Patients With SARS-CoV-2 at a Smart Quarantine Station: Observational Study (Preprint)

2020 ◽  
Author(s):  
Ping-Yen Liu ◽  
Yi-Shan Tsai ◽  
Po-Lin Chen ◽  
Huey-Pin Tsai ◽  
Ling-Wei Hsu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Observational Study ◽  
Processing Time ◽  
Clinical Decision Making ◽  
Image Interpretation ◽  
Clinical Decision ◽  
Tablet Computer ◽  
X Rays ◽  
Quarantine Station

BACKGROUND As the COVID-19 epidemic increases in severity, the burden of quarantine stations outside emergency departments (EDs) at hospitals is increasing daily. To address the high screening workload at quarantine stations, all staff members with medical licenses are required to work shifts in these stations. Therefore, it is necessary to simplify the workflow and decision-making process for physicians and surgeons from all subspecialties. OBJECTIVE The aim of this paper is to demonstrate how the National Cheng Kung University Hospital artificial intelligence (AI) trilogy of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm improves medical care and reduces quarantine processing times. METHODS This observational study on the emerging COVID-19 pandemic included 643 patients. An “AI trilogy” of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm on a tablet computer was applied to shorten the quarantine survey process and reduce processing time during the COVID-19 pandemic. RESULTS The use of the AI trilogy facilitated the processing of suspected cases of COVID-19 with or without symptoms; also, travel, occupation, contact, and clustering histories were obtained with the tablet computer device. A separate AI-mode function that could quickly recognize pulmonary infiltrates on chest x-rays was merged into the smart clinical assisting system (SCAS), and this model was subsequently trained with COVID-19 pneumonia cases from the GitHub open source data set. The detection rates for posteroanterior and anteroposterior chest x-rays were 55/59 (93%) and 5/11 (45%), respectively. The SCAS algorithm was continuously adjusted based on updates to the Taiwan Centers for Disease Control public safety guidelines for faster clinical decision making. Our ex vivo study demonstrated the efficiency of disinfecting the tablet computer surface by wiping it twice with 75% alcohol sanitizer. To further analyze the impact of the AI application in the quarantine station, we subdivided the station group into groups with or without AI. Compared with the conventional ED (n=281), the survey time at the quarantine station (n=1520) was significantly shortened; the median survey time at the ED was 153 minutes (95% CI 108.5-205.0), vs 35 minutes at the quarantine station (95% CI 24-56; <i>P</i>&lt;.001). Furthermore, the use of the AI application in the quarantine station reduced the survey time in the quarantine station; the median survey time without AI was 101 minutes (95% CI 40-153), vs 34 minutes (95% CI 24-53) with AI in the quarantine station (<i>P</i>&lt;.001). CONCLUSIONS The AI trilogy improved our medical care workflow by shortening the quarantine survey process and reducing the processing time, which is especially important during an emerging infectious disease epidemic.

The Use of Artificial Intelligence Systems for Support of Medical Decision-Making

2011 ◽  
pp. 1017-1029
Author(s):  
William Claster ◽  
Nader Ghotbi ◽  
Subana Shanmuganathan
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Statistical Analysis ◽  
Medical Records ◽  
Clinical Decision Making ◽  
Ct Scanning ◽  
Clinical Decision ◽  
Radiology Department ◽  
Self Organizing Map ◽  
One Year

There is a treasure trove of hidden information in the textual and narrative data of medical records that can be deciphered by text-mining techniques. The information provided by these methods can provide a basis for medical artificial intelligence and help support or improve clinical decision making by medical doctors. In this paper we extend previous work in an effort to extract meaningful information from free text medical records. We discuss a methodology for the analysis of medical records using some statistical analysis and the Kohonen Self-Organizing Map (SOM). The medical data derive from about 700 pediatric patients’ radiology department records where CT (Computed Tomography) scanning was used as part of a diagnostic exploration. The patients underwent CT scanning (single and multiple) throughout a one-year period in 2004 at the Nagasaki University Medical Hospital. Our approach led to a model based on SOM clusters and statistical analysis which may suggest a strategy for limiting CT scan requests. This is important because radiation at levels ordinarily used for CT scanning may pose significant health risks especially to children.

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

2020 ◽  
pp. 167-186
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Classification Framework

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

scholarly journals Routine Follow-Up Radiographs for Ankle Fractures Seldom Add Value to Clinical Decision-Making: A Retrospective, Observational Study

2018 ◽  
Vol 57 (5) ◽  
pp. 957-960 ◽  
Author(s):  
Pieter van Gerven ◽  
Nikki L. Weil ◽  
Marco F. Termaat ◽  
Sidney M. Rubinstein ◽  
Mostafa El Moumni ◽  
...  
Keyword(s):  
Decision Making ◽  
Observational Study ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Ankle Fractures ◽  
Retrospective Observational Study

scholarly journals The Role of Artificial Intelligence in Many Dental Specialties

2021 ◽  
Vol 41 ◽  
pp. 03005
Author(s):  
Choirunisa Nur Humairo ◽  
Aquarina Hapsari ◽  
Indra Bramanti
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Clinic Management ◽  
Pediatric Dentist ◽  
Dental Procedures ◽  
Evolution Of Technology ◽  
Near Future

Background: Technology has become a fundamental part of human living. The evolution of technology has been advantageous to science development, including dentistry. One of the latest technology that draw many attention is Artificial Intelligence (AI). Purpose: The aim of this review is to explain the use of AI in many disciplines of dental specialties and its benefit. Reviews: The application of Artificial Intelligence may be beneficial for all dental specialties, varying from pediatric dentist to oral surgeon. In dental clinic management, AI may assist in medical record as well as other paperwork. AI would also give a valuable contribution in important dental procedures, such as diagnosis and clinical decision making. It helps the dentist deliver the best treatment for the patients. Conclusion: The latest development of Artificial Intelligence is beneficial for dental practitioner in the near future. It is considered as a breakthrough of the 21st century to support the diagnostic procedure and decision making in clinical practice. The use of AI can be applied in most of dental specialties.

Artificial Intelligence in Lung Cancer: Bridging the Gap Between Computational Power and Clinical Decision-Making

2020 ◽  
pp. 084653712094143
Author(s):  
Jaryd R. Christie ◽  
Pencilla Lang ◽  
Lauren M. Zelko ◽  
David A. Palma ◽  
Mohamed Abdelrazek ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Biomarker Discovery ◽  
Surgical Techniques ◽  
Clinical Decision ◽  
Clinical Implementation ◽  
Cancer Management

Lung cancer remains the most common cause of cancer death worldwide. Recent advances in lung cancer screening, radiotherapy, surgical techniques, and systemic therapy have led to increasing complexity in diagnosis, treatment decision-making, and assessment of recurrence. Artificial intelligence (AI)–based prediction models are being developed to address these issues and may have a future role in screening, diagnosis, treatment selection, and decision-making around salvage therapy. Imaging plays an essential role in all components of lung cancer management and has the potential to play a key role in AI applications. Artificial intelligence has demonstrated value in prognostic biomarker discovery in lung cancer diagnosis, treatment, and response assessment, putting it at the forefront of the next phase of personalized medicine. However, although exploratory studies demonstrate potential utility, there is a need for rigorous validation and standardization before AI can be utilized in clinical decision-making. In this review, we will provide a summary of the current literature implementing AI for outcome prediction in lung cancer. We will describe the anticipated impact of AI on the management of patients with lung cancer and discuss the challenges of clinical implementation of these techniques.

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