scholarly journals The Value of Artificial Intelligence in Laboratory Medicine

2020 ◽  
Author(s):  
Ketan Paranjape ◽  
Michiel Schinkel ◽  
Richard D Hammer ◽  
Bo Schouten ◽  
R S Nannan Panday ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Laboratory Medicine ◽  
Medical Community ◽  
Barriers To Adoption ◽  
Web Based ◽  
Medical Practitioners ◽  
Privacy Concerns ◽  
Key Stakeholders ◽  
Clinical Benefits ◽  
High Investment

Abstract Objectives As laboratory medicine continues to undergo digitalization and automation, clinical laboratorians will likely be confronted with the challenges associated with artificial intelligence (AI). Understanding what AI is good for, how to evaluate it, what are its limitations, and how it can be implemented are not well understood. With a survey, we aimed to evaluate the thoughts of stakeholders in laboratory medicine on the value of AI in the diagnostics space and identify anticipated challenges and solutions to introducing AI. Methods We conducted a web-based survey on the use of AI with participants from Roche’s Strategic Advisory Network that included key stakeholders in laboratory medicine. Results In total, 128 of 302 stakeholders responded to the survey. Most of the participants were medical practitioners (26%) or laboratory managers (22%). AI is currently used in the organizations of 15.6%, while 66.4% felt they might use it in the future. Most had an unsure attitude on what they would need to adopt AI in the diagnostics space. High investment costs, lack of proven clinical benefits, number of decision makers, and privacy concerns were identified as barriers to adoption. Education in the value of AI, streamlined implementation and integration into existing workflows, and research to prove clinical utility were identified as solutions needed to mainstream AI in laboratory medicine. Conclusions This survey demonstrates that specific knowledge of AI in the medical community is poor and that AI education is much needed. One strategy could be to implement new AI tools alongside existing tools.

scholarly journals Deconstructing Canada’s efforts to integrate artificial intelligence in medicine and medical education

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Semsi Kocabas ◽  
Elif Bilgic ◽  
Andrew Gorgy ◽  
Jason Harley
Keyword(s):  
Artificial Intelligence ◽  
Medical Education ◽  
Extracurricular Activities ◽  
Interdisciplinary Collaboration ◽  
Critical Role ◽  
Medical Community ◽  
Undergraduate Medical Students ◽  
Medical Practitioners ◽  
Medical Associations ◽  
New Policies

Artificial intelligence (AI) has gained momentum in the last decade in various professional domains, but its usage remains scarce in the field of medicine. Available AI-enhanced devices are not integrated in a consistent fashion throughout Canadian health facilities, and current medical practitioners and students are not well prepared for AI’s impact on their careers. Undergraduate medical students lack fundamental knowledge of AI in medicine, from its impact on patient care and its potential as an adjunct decision-making tool, to the general fundamentals of how AI-enhanced devices work. Currently, postgraduates don’t have access to AI-enhanced devices; this could potentially limit their understanding of how these devices might affect their future clinical practice. Canadian medical universities can play a critical role in familiarizing students with these new devices. Incorporating new topics into the already heavily charged medical curricula may be challenging, but students could make use of extracurricular activities to learn the concept of AI and strengthen interdisciplinary collaboration. Educational institutions would also need to propose policies for the safe and ethical use of devices in classrooms or internships. However, they might require guidance to draft new policies targeting AI in medical education. Canadian medical associations could take the lead to draft AI policies in healthcare to guide the equal and safe implementation of AI-enhanced devices across the Canadian medical community. Our paper will explore the work that has been done related to AI-specific policies in healthcare, focusing on Canada, and provide key points that could be used to organize future policies.

scholarly journals Role of Laboratory Medicine in SARS-CoV-2 Diagnostics. Lessons Learned from a Pandemic

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 915
Author(s):  
Irena Duś-Ilnicka ◽  
Aleksander Szymczak ◽  
Małgorzata Małodobra-Mazur ◽  
Miron Tokarski
Keyword(s):  
Molecular Biology ◽  
Real Time ◽  
Real Time Pcr ◽  
Laboratory Medicine ◽  
Lessons Learned ◽  
Diagnostic Methods ◽  
Medical Community ◽  
Medical Diagnostic ◽  
Novel Coronavirus ◽  
Available Information

Since the 2019 novel coronavirus outbreak began in Wuhan, China, diagnostic methods in the field of molecular biology have been developing faster than ever under the vigilant eye of world’s research community. Unfortunately, the medical community was not prepared for testing such large volumes or ranges of biological materials, whether blood samples for antibody immunological testing, or salivary/swab samples for real-time PCR. For this reason, many medical diagnostic laboratories have made the switch to working in the field of molecular biology, and research undertaken to speed up the flow of samples through laboratory. The aim of this narrative review is to evaluate the current literature on laboratory techniques for the diagnosis of SARS-CoV-2 infection available on pubmed.gov, Google Scholar, and according to the writers’ knowledge and experience of the laboratory medicine. It assesses the available information in the field of molecular biology by comparing real-time PCR, LAMP technique, RNA sequencing, and immunological diagnostics, and examines the newest techniques along with their limitations for use in SARS-CoV-2 diagnostics.

scholarly journals The Ethics of Artificial Intelligence in Pathology and Laboratory Medicine: Principles and Practice

2021 ◽  
Vol 8 ◽  
pp. 237428952199078
Author(s):  
Brian R. Jackson ◽  
Ye Ye ◽  
James M. Crawford ◽  
Michael J. Becich ◽  
Somak Roy ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Laboratory Medicine ◽  
Risks And Benefits ◽  
Scientific Ethics

Growing numbers of artificial intelligence applications are being developed and applied to pathology and laboratory medicine. These technologies introduce risks and benefits that must be assessed and managed through the lens of ethics. This article describes how long-standing principles of medical and scientific ethics can be applied to artificial intelligence using examples from pathology and laboratory medicine.

Use of smartphone apps in bipolar disorder: An international web-based survey of feature preferences and privacy concerns.

2021 ◽  
Author(s):  
Emma Morton ◽  
Jennifer Nicholas ◽  
Laura Lapadat ◽  
Heather L. O'Brien ◽  
Steven J. Barnes ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Smartphone Apps ◽  
Web Based ◽  
Privacy Concerns

Artificial intelligence-based automatic visual inspection system for built heritage

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lukman E. Mansuri ◽  
D.A. Patel
Keyword(s):  
Artificial Intelligence ◽  
Visual Inspection ◽  
Image Data ◽  
Inspection System ◽  
Data Set ◽  
Web Based ◽  
Content Type ◽  
Built Heritage ◽  
Automatic Visual Inspection ◽  
Inspection Systems

PurposeHeritage is the latent part of a sustainable built environment. Conservation and preservation of heritage is one of the United Nations' (UN) sustainable development goals. Many social and natural factors seriously threaten heritage structures by deteriorating and damaging the original. Therefore, regular visual inspection of heritage structures is necessary for their conservation and preservation. Conventional inspection practice relies on manual inspection, which takes more time and human resources. The inspection system seeks an innovative approach that should be cheaper, faster, safer and less prone to human error than manual inspection. Therefore, this study aims to develop an automatic system of visual inspection for the built heritage.Design/methodology/approachThe artificial intelligence-based automatic defect detection system is developed using the faster R-CNN (faster region-based convolutional neural network) model of object detection to build an automatic visual inspection system. From the English and Dutch cemeteries of Surat (India), images of heritage structures were captured by digital camera to prepare the image data set. This image data set was used for training, validation and testing to develop the automatic defect detection model. While validating this model, its optimum detection accuracy is recorded as 91.58% to detect three types of defects: “spalling,” “exposed bricks” and “cracks.”FindingsThis study develops the model of automatic web-based visual inspection systems for the heritage structures using the faster R-CNN. Then it demonstrates detection of defects of spalling, exposed bricks and cracks existing in the heritage structures. Comparison of conventional (manual) and developed automatic inspection systems reveals that the developed automatic system requires less time and staff. Therefore, the routine inspection can be faster, cheaper, safer and more accurate than the conventional inspection method.Practical implicationsThe study presented here can improve inspecting the built heritages by reducing inspection time and cost, eliminating chances of human errors and accidents and having accurate and consistent information. This study attempts to ensure the sustainability of the built heritage.Originality/valueFor ensuring the sustainability of built heritage, this study presents the artificial intelligence-based methodology for the development of an automatic visual inspection system. The automatic web-based visual inspection system for the built heritage has not been reported in previous studies so far.

Software Tools for Systematic Literature Review in Medicine: A Review and Feature Analysis (Preprint)

2021 ◽  
Author(s):  
Kathryn Cowie ◽  
Asad Rahmatullah ◽  
Nicole Hardy ◽  
Karl Holub ◽  
Kevin Kallmes
Keyword(s):  
Best Practices ◽  
Software Tools ◽  
Observer Agreement ◽  
Medical Community ◽  
Feature Analysis ◽  
Data Abstraction ◽  
Statistical Software ◽  
Web Based ◽  
Knowledge Support ◽  
Dual Extraction

BACKGROUND Systematic reviews (SRs) are central to evaluating therapies but have high costs in terms of both time and money. Many software tools exist to assist with SRs, but most tools do not support the full process, and transparency and replicability of SR depends on performing and presenting evidence according to established best practices. OBJECTIVE In order to provide a basis for comparing and selecting between software tools that support SR, we performed a feature-by-feature comparison of SR tools. METHODS We searched for SR tools by reviewing any such tool listed the Systematic Review Toolbox, previous reviews of SR tools, and qualitative Google searching. We included all SR tools that were currently functional, and require no coding and excluded reference managers, desktop applications, and statistical software. The list of features to assess was populated by combining all features assessed in four previous reviews of SR tools; we also added five features (Manual Addition, Screening Automation, Dual Extraction, Living review, Public outputs) that were independently noted as best practices or enhancements of transparency/replicability. Then, two reviewers assigned binary “present/absent” assessments to all SR tools with respect to all features, and a third reviewer adjudicated all disagreements. RESULTS Of 49 SR tools found, 27 were excluded, leaving 22 for assessment. Twenty-eight features were assessed across 6 classes, and the inter-observer agreement was 86.46%. DistillerSR, EPPI-Reviewer Web, and Nested Knowledge support the most features (24/28, 86%), followed by Covidence, SRDB.PRO, SysRev (20/28, 71%). Six tools support fewer than half of all features assessed: SyRF, Data Abstraction Assistant, SWIFT-review, SR-Accelerator, RobotReviewer, and COVID-NMA. Notably, only 9 of 22 tools (41%) support direct search, only four (18%) offer dual extraction, and only 9 (41%) offer living/updatable reviews. CONCLUSIONS DistillerSR, EPPI-Reviewer Web, and Nested Knowledge each offer a high density of SR-focused web-based tools. By transparent comparison and discussion regarding SR tool functionality, the medical community can both choose among existing software offerings and note the areas of growth needed, most notably in the support of living reviews.

scholarly journals Investigating Market and Regulatory Forces Shaping Artificial Intelligence Adoptions

10.28945/4644 ◽  
2020 ◽  
Vol 4 ◽  
pp. 177-192
Author(s):  
Chrissann R. Ruehle
Keyword(s):  
Artificial Intelligence ◽  
Research Question ◽  
Large Firms ◽  
High Performing ◽  
Key Stakeholders ◽  
The Media ◽  
Management Capabilities ◽  
Collection And Management ◽  
Future Growth

The Artificial Intelligence (AI) industry has experienced tremendous growth in recent years. Consequently, there has been considerable hype, interest, and even misinformation in the media regarding this emergent technology. Practitioners and academics alike are interested in learning how this market functions in order to make evidence-based decisions regarding its adoption. The purpose of this manuscript is to perform a systematic examination of the current market dynamics as well as identify future growth opportunities for the benefit of incumbents in addition to firms seeking to enter the AI market. The primary research question is: how do market and governmental forces reportedly shape AI adoptions? Drawing on predominantly practitioner focused literature, along with several seminal academic sources, the article begins by examining and mapping stakeholders in the market. This approach allows for the identification and analysis of key stakeholders. Semiconductor and cloud computing firms play a substantive role in the AI adoption ecosystem as they wield substantial power as revealed in this analysis. Subsequently, the TOE framework, which includes the technology, organization and environmental contexts, is applied in order to understand the role of these forces in shaping the AI market. This analysis demonstrates that large firms have a significant competitive advantage due to their extensive data collection and management capabilities in addition to attracting data scientists and high performing analytics professionals. Large firms are actively acquiring small and medium sized AI businesses in order to expand their offerings, particularly in dynamic emerging fields such as facial recognition technology and deep learning.

scholarly journals Physicians’ requirements and expectations of future Artificial Intelligence applications for healthcare – a web-based survey in German university hospitals (Preprint)

2020 ◽  
Author(s):  
Oliver Maassen ◽  
Sebastian Fritsch ◽  
Julia Gantner ◽  
Saskia Deffge ◽  
Julian Kunze ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Patient Care ◽  
Biomedical Research ◽  
Positive Attitude ◽  
Patient Data ◽  
University Hospitals ◽  
Web Based ◽  
Wide Range ◽  
Medical Disciplines

BACKGROUND The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of healthcare. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians’ requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research has not been investigated widely in German university hospitals. OBJECTIVE Evaluate physicians’ requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research e.g. for the development of machine learning (ML) algorithms in university hospitals in Germany. METHODS A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given on Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. RESULTS 121 (39.9%) female and 173 (57.1%) male physicians (N=303) from a wide range of medical disciplines and work experience levels completed the online survey. The majority of respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). 82.5% of respondents (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. CONCLUSIONS Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism, there come several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (e.g., imaging procedures in radiology and pathology) or data is collected continuously (e.g. cardiology and intensive care medicine), physicians’ expectations to substantially improve future patient care are high. However, for the practical usage of AI in healthcare regulatory and organizational challenges still have to be mastered.

scholarly journals Misconceptions in the health technology industry that are delaying the translation of artificial intelligence technology into relevant clinical applications

2021 ◽  
Vol 54 (4) ◽  
pp. 243-245
Author(s):  
Fabíola Macruz
Keyword(s):  
Artificial Intelligence ◽  
Human Performance ◽  
Health Technology ◽  
Medical Community ◽  
Technology Industry ◽  
Diagnosis And Prognosis ◽  
Starting Point ◽  
Causative Factors ◽  
The Status ◽  
Artificial Intelligence Technology

Abstract There is great optimism that artificial intelligence (AI), as it disrupts the medical world, will provide considerable improvements in all areas of health care, from diagnosis to treatment. In addition, there is considerable evidence that AI algorithms have surpassed human performance in various tasks, such as analyzing medical images, as well as correlating symptoms and biomarkers with the diagnosis and prognosis of diseases. However, the mismatch between the performance of AI-based software and its clinical usefulness is still a major obstacle to its widespread acceptance and use by the medical community. In this article, three fundamental concepts observed in the health technology industry are highlighted as possible causative factors for this gap and might serve as a starting point for further evaluation of the structure of AI companies and of the status quo.

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