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 Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making - A systematic review

2021 ◽  
Vol 16 (1) ◽  
pp. 482-492
Author(s):  
Sanjeev B. Khanagar ◽  
Ali Al-Ehaideb ◽  
Satish Vishwanathaiah ◽  
Prabhadevi C. Maganur ◽  
Shankargouda Patil ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Decision Making ◽  
Treatment Planning ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Artificial Intelligence Technology ◽  
And Performance

scholarly journals ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging

2021 ◽  
Author(s):  
Tobias Penzkofer ◽  
Anwar R. Padhani ◽  
Baris Turkbey ◽  
Masoom A. Haider ◽  
Henkjan Huisman ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Cancer Diagnosis ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Disease Prevalence ◽  
Prostate Cancer Diagnosis ◽  
Detection Systems ◽  
Initial Work

Abstract Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists’ workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis. Key Points • AI systems need to ensure that the benefits of biopsy avoidance are delivered with consistent high specificities, at a range of disease prevalence. • Initial work has focused on developing systems as diagnostic supporting aids for outlining tasks, so they can be integrated into the radiologists’ workflow to support MRI-directed biopsies. • Decision support tools require a larger body of work including multicentric, multivendor studies where the clinical needs, disease prevalence, patient preferences, and clinical setting are additionally defined.

Artificial intelligence in modern medicine- evolving necessity of the present and role in transforming the future of medical care (Preprint)

2020 ◽  
Author(s):  
Pradnya Brijmohan Bhattad ◽  
Vinay Jain
Keyword(s):  
Artificial Intelligence ◽  
Clinical Decision Making ◽  
Computer Systems ◽  
Clinical Decision ◽  
Modern Medicine ◽  
Machine Learning Techniques ◽  
Diagnostic Systems ◽  
Diagnosis And Prognosis ◽  
Digital World ◽  
Artificial Intelligence Technology

UNSTRUCTURED The dexterity of computer systems to resemble and mimic human intelligence is artificial intelligence. Artificial intelligence has reformed the diagnostic and therapeutic precision and competence in various fields of medicine. Artificial intelligence appears to play a bright role in the medical diagnosis. Computer systems using artificial intelligence help in assessment of medical images and enormous data. This research aims to identify how the artificial intelligence-based technology is reforming the art of medicine. Artificial intelligence empowers providers in improving efficiency and overall healthcare. Newer machine learning techniques lead the automatic diagnostic systems. Areas of medicine such as medical imaging, automated clinical decision-making support have made significant advances with respect to artificial intelligence technology. With improved diagnosis and prognosis, artificial intelligence possesses the capability to revolutionize various fields of medicine. Artificial intelligence has its own limitations and cannot replace a bedside clinician. In the evolving modern medical digital world, physicians need to support artificial intelligence rather than fear it replacing trained physicians for improved healthcare.

Perceived Effectiveness of Artificial Intelligence based Decision Support System in a Clinical Setting: A Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Avishek Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Support System ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Theoretical Perspective ◽  
Care Providers

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.

scholarly journals AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity

2021 ◽  
Vol 10 (4) ◽  
pp. 766 ◽  
Author(s):  
Ljiljana Trtica Majnarić ◽  
František Babič ◽  
Shane O’Sullivan ◽  
Andreas Holzinger
Keyword(s):  
Big Data ◽  
Clinical Decision Making ◽  
Big Data Analytics ◽  
Clinical Decision ◽  
Radical Change ◽  
Research Approach ◽  
Classification Problems ◽  
Care Needs ◽  
Healthcare Settings ◽  
Interaction Factors

Multimorbidity refers to the coexistence of two or more chronic diseases in one person. Therefore, patients with multimorbidity have multiple and special care needs. However, in practice it is difficult to meet these needs because the organizational processes of current healthcare systems tend to be tailored to a single disease. To improve clinical decision making and patient care in multimorbidity, a radical change in the problem-solving approach to medical research and treatment is needed. In addition to the traditional reductionist approach, we propose interactive research supported by artificial intelligence (AI) and advanced big data analytics. Such research approach, when applied to data routinely collected in healthcare settings, provides an integrated platform for research tasks related to multimorbidity. This may include, for example, prediction, correlation, and classification problems based on multiple interaction factors. However, to realize the idea of this paradigm shift in multimorbidity research, the optimization, standardization, and most importantly, the integration of electronic health data into a common national and international research infrastructure is needed. Ultimately, there is a need for the integration and implementation of efficient AI approaches, particularly deep learning, into clinical routine directly within the workflows of the medical professionals.

scholarly journals Ondansetron use is associated with lower COVID-19 mortality in a Real-World Data network-based analysis

2021 ◽  
Author(s):  
Gregory M Miller ◽  
Austin J Ellis ◽  
Rangaprasad Sarangarajan ◽  
Amay Parikh ◽  
Leonardo O Rodrigues ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Long Term Effects ◽  
Real World Data ◽  
World Data ◽  
Antiemetic Agent ◽  
Future Loss ◽  
Selection Operator

Objective: The COVID-19 pandemic generated a massive amount of clinical data, which potentially holds yet undiscovered answers related to COVID-19 morbidity, mortality, long term effects, and therapeutic solutions. The objective of this study was to generate insights on COVID-19 mortality-associated factors and identify potential new therapeutic options for COVID-19 patients by employing artificial intelligence analytics on real-world data. Materials and Methods: A Bayesian statistics-based artificial intelligence data analytics tool (bAIcis®) within Interrogative Biology® platform was used for network learning, inference causality and hypothesis generation to analyze 16,277 PCR positive patients from a database of 279,281 inpatients and outpatients tested for SARS-CoV-2 infection by antigen, antibody, or PCR methods during the first pandemic year in Central Florida. This approach generated causal networks that enabled unbiased identification of significant predictors of mortality for specific COVID-19 patient populations. These findings were validated by logistic regression, regression by least absolute shrinkage and selection operator, and bootstrapping. Results: We found that in the SARS-CoV-2 PCR positive patient cohort, early use of the antiemetic agent ondansetron was associated with increased survival in mechanically ventilated patients. Conclusions: The results demonstrate how real world COVID-19 focused data analysis using artificial intelligence can generate valid insights that could possibly support clinical decision-making and minimize the future loss of lives and resources.

scholarly journals Toward harmonization of clinical molecular diagnostic reports: findings of an international survey

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Deborah A. Payne ◽  
Katarina Baluchova ◽  
Graciela Russomando ◽  
Parviz Ahmad-Nejad ◽  
Cyril Mamotte ◽  
...  
Keyword(s):  
Molecular Diagnostics ◽  
Clinical Decision Making ◽  
Clinical Chemistry ◽  
Clinical Decision ◽  
Molecular Diagnostic ◽  
Molecular Testing ◽  
End User ◽  
Iso 15189 ◽  
Reporting Practices ◽  
Reporting Phase

Abstract Background: The International Organization for Standardization (ISO) 15189 standard provides recommendations for the postexamination reporting phase to enhance quality in clinical laboratories. The purpose of this study was to encourage a broad discussion on current reporting practices for molecular diagnostic tests by conducting a global survey of such practices. Methods: The International Federation of Clinical Chemistry and Laboratory Medicine’s Committee for Molecular Diagnostics (IFCC C-MD) surveyed laboratories on selected ISO 15189 recommendations and topics. The survey addressed the following aspects: (1) laboratory demographics, (2) report format, (3) result reporting/layout, (4) comments in report and (5) interpretation and clinical decision-making information. Additionally, participants indicated categories needing standardization. Results: Sixteen responses from laboratories located in Asia, Europe, the Middle East, North America and South America were received. Several categories yielded 100% agreement between laboratories, whereas other categories had less than or equal to 50% concordance. Participants scored “nomenclature” and “description of methodologies” as the two most frequently cited aspects needing standardization. Conclusions: The postexamination phase requires extensive and consistent communication between the laboratory, the healthcare provider and the end user. Surveyed laboratories were most likely to follow explicit ISO 15189 recommendations vs. recommendations when the term(s) “where appropriate or where applicable” was used. Interpretation and reporting of critical values varied among participants. Although the outcome of this study may not fully represent the practices of all molecular testing laboratories in countries around the world, the survey identified and specified several recommendations that are requirements for harmonized reporting in molecular diagnostics.

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.

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