Search

Artificial intelligence (AI) in breast imaging is a rapidly developing field with promising results. Despite the large number of recent publications in this field, unanswered questions have led to limited implementation of AI into daily clinical practice for breast radiologists. This paper provides an overview of the key limitations of AI in breast imaging including, but not limited to, limited numbers of FDA-approved algorithms and annotated data sets with histologic ground truth; concerns surrounding data privacy, security, algorithm transparency, and bias; and ethical issues. Ultimately, the successful implementation of AI into clinical care will require thoughtful action to address these challenges, transparency, and sharing of AI implementation workflows, limitations, and performance metrics within the breast imaging community and other end-users. [ABSTRACT FROM AUTHOR]

Background Music is an integral part of our lives and is often played in public places like restaurants. People exposed to music that contained alcohol-related lyrics in a bar scenario consumed significantly more alcohol than those exposed to music with less alcohol-related lyrics. Existing methods to quantify alcohol exposure in song lyrics have used manual annotation that is burdensome and time intensive. In this paper, we aim to build a deep learning algorithm (LYDIA) that can automatically detect and identify alcohol exposure and its context in song lyrics. Methods We identified 673 potentially alcohol-related words including brand names, urban slang, and beverage names. We collected all the lyrics from the Billboard's top-100 songs from 1959 to 2020 (N = 6110). We developed an annotation tool to annotate both the alcohol-relation of the word (alcohol, non-alcohol, or unsure) and the context (positive, negative, or neutral) of the word in the song lyrics. Results LYDIA achieved an accuracy of 86.6% in identifying the alcohol-relation of the word, and 72.9% in identifying its context. LYDIA can distinguish with an accuracy of 97.24% between the words that have positive and negative relation to alcohol; and with an accuracy of 98.37% between the positive and negative context. Conclusion LYDIA can automatically identify alcohol exposure and its context in song lyrics, which will allow for the swift analysis of future lyrics and can be used to help raise awareness about the amount of alcohol in music. Highlights Developed a deep learning algorithm (LYDIA) to identify alcohol words in songs. LYDIA achieved an accuracy of 86.6% in identifying alcohol-relation of the words. LYDIA's accuracy in identifying positive, negative, or neutral context was 72.9%. LYDIA can automatically provide evidence of alcohol in millions of songs. This can raise awareness of harms of listening to songs with alcohol words. [ABSTRACT FROM AUTHOR]

In diagnostic medicine, the true disease status of a patient is often represented on an ordinal scale—for example, cancer stage (0, I, II, III, or IV) or coronary artery disease severity measured using the Coronary Artery Disease Reporting and Data System (CAD-RADS) scale (none, minimal, mild, moderate, severe, or occluded). With advances in quantitation of diagnostic images and in artificial intelligence (AI), both supervised and unsupervised algorithms are being developed to help physicians correctly grade disease. Most of the diagnostic accuracy literature deals with binary disease status (disease present or absent); however, tests diagnosing ordinal-scaled diseases should not be reduced to a binary status just to simplify diagnostic accuracy testing. In this paper, we propose different characterizations of ordinal-scale accuracy for different clinical use scenarios, along with methods for comparing tests. In the simplest scenario, just the proportion of correct grades is considered; other scenarios address the magnitude and direction of misgrading; and at the other extreme, a weighted accuracy measure with weights based on the relative costs of different types of misgrading is presented. The various scenarios are illustrated using a coronary artery disease example where the accuracy of AI algorithms in providing patients with the correct CAD-RADS grade is assessed. [ABSTRACT FROM AUTHOR]

Objective: As the number of radiology artificial intelligence (AI) papers increases, there are new challenges for reviewing the AI literature as well as differences to be aware of, for those familiar with the clinical radiology literature. We aim to introduce a tool to aid in this process. Methods: In evidence‐based practise (EBP), you must Ask, Search, Appraise, Apply and Evaluate to come to an evidence‐based decision. The bottom‐up evidence‐based radiology (EBR) method allows for a systematic way of choosing the correct radiological investigation or treatment. Just as the population intervention comparison outcome (PICO) method is an established means of asking an answerable question; herein, we introduce the data algorithm training output (DATO) method to complement PICO by considering Data, Algorithm, Training and Output in the use of AI to answer the question. Results: We illustrate the DATO method with a worked example concerning bone age assessment from skeletal radiographs. After a systematic search, 17 bone age estimation papers (5 of which externally validated their results) were appraised. The paper with the best DATO metrics found that an ensemble model combining uncorrelated, high performing simple models should achieve error rates comparable to human performance. Conclusion: Considering DATO in the application of EBR to AI is a simple systematic approach to this potentially daunting subject. Advances in knowledge: The growth of AI in radiology means that radiologists and related professionals now need to be able to review not only clinical radiological literature but also research using AI methods. Considering Data, Algorithm, Training and Output in the application of EBR to AI is a simple systematic approach to this potentially daunting subject. [ABSTRACT FROM AUTHOR]

Objective. The Fugl-Meyer assessment for upper extremity (FMA-UE) is a measure for assessing upper extremity motor function in patients with stroke. However, the considerable administration time of the assessment decreases its feasibility. This study aimed to develop an accumulative assessment system of upper extremity motor function (AAS-UE) based on the FMA-UE to improve administrative efficiency while retaining sufficient psychometric properties. Methods. The study used secondary data from 3 previous studies having FMA-UE datasets, including 2 follow-up studies for subacute stroke individuals and 1 test-retest study for individuals with chronic stroke. The AAS-UE adopted deep learning algorithms to use patients' prior information (ie, the FMA-UE scores in previous assessments, time interval of adjacent assessments, and chronicity of stroke) to select a short and personalized item set for the following assessment items and reproduce their FMA-UE scores. Results. Our data included a total of 682 patients after stroke. The AAS-UE administered 10 different items for each patient. The AAS-UE demonstrated good concurrent validity (r =0.97-0.99 with the FMA-UE), high test-retest reliability (intra-class correlation coefficient=0.96), low random measurement error (percentage of minimal detectable change=15.6%), good group-level responsiveness (standardized response mean=0.65-1.07), and good individual-level responsiveness (30.5%-53.2% of patients showed significant improvement). These psychometric properties were comparable to those of the FMA-UE. Conclusion. The AAS-UE uses an innovative assessment method, which makes good use of patients' prior information to achieve administrative efficiency with good psychometric properties. Impact. This study demonstrates a new assessment method to improve administrative efficiency while retaining psychometric properties, especially individual-level responsiveness and random measurement error, by making good use of patients' basic information and medical records. [ABSTRACT FROM AUTHOR]

Primary care is a growing medical field willing to become a more integrated and technological asset. Although big changes already happened and more investments have been made, a limited amount of literature describes processes and technologies there applied or to be used. This paper aims to evaluate the efficacy and usability of different types of algorithms in primary care to improve drug safety by speeding up processes and achieving greater accuracy opening the path to providing better healthcare overall. The PICO model was adopted, three electronic databases (PubMed, Cochrane, Web of Science) were searched using appropriate keywords. Selected studies were assessed for quality and risk of bias using the National Institutes of Health Quality Assessment of Controlled Intervention Studies. Data were analysed using descriptive statistic, comparison of drug usage between algorithms or artificial intelligence application and usual care was performed using a X² test(α= 0.05). Out of 2207,19 studies were included,37% of them regarding error prevention,21% drug interactions,21% drug monitoring,16% drug prescription,5% drug administration. Results showed an easier and safer medication use in 74% of studies, a loss of safeness and accuracy in 16%; 10% of total studies did not come up with a valid esteem of results either for inadequate availability of the AI machine or because of the heterogeneity of the results in different settings. The evaluation of errors prevention, 40% of total studies, showed the most statistically significant results with 88% of positive outcomes from AI application. The results support that this technological approach to drugs management could contribute to the safety of treatment and to increase patients’ and general practitioners’ satisfaction. The application of AI or algorithms is significantly associated with a reduction of drug use errors (p < 0.05). Future studies should work toward establishing a gold standard to measure AI performances. Key messages: · Artificial Intelligence and Algorithms in primary care have the potentiality to disrupt patient care with a safer and faster medication management. · A comparison between Artificial Intelligence or algorithms and standard clinical practice may help finding the medication fields where a technological support could lead to better results. [ABSTRACT FROM AUTHOR]

AI can make mistakes and cause unfavorable consequences. It is important to know how people react to such AI-driven negative consequences and subsequently evaluate the fairness of AI's decisions. This study theorizes and empirically tests two psychological mechanisms that explain the process: (a) heuristic expectations of AI's consistent performance (automation bias) and subsequent frustration of unfulfilled expectations (algorithmic aversion) and (b) heuristic perceptions of AI's controllability over negative results. Our findings from two experimental studies reveal that these two mechanisms work in an opposite direction. First, participants tend to display more sensitive responses to AI's inconsistent performance and thus make more punitive assessments of AI's decision fairness, when compared to responses to human experts. Second, as participants perceive AI has less control over unfavorable outcomes than human experts, they are more tolerant in their assessments of AI. [ABSTRACT FROM AUTHOR]

Digitization in transplantation is not a new phenomenon. Algorithms are being used, for example, to allocate organs based on medical compatibility and priority criteria. However, digitization is accelerating as computer scientists and physicians increasingly develop and use machine learning (ML) models to obtain better predictions on the chances of a successful transplant. The objective of the article is to shed light on the potential threats to equitable access to organs allocated through algorithms, whether these are the consequence of political choices made upstream of digitization or of the algorithmic design, or are produced by self-learning algorithms. The article shows that achieving equitable access requires an overall vision of the algorithmic development process and that European legal norms only partially contribute to preventing harm and addressing equality in access to organs. [ABSTRACT FROM AUTHOR]

Countries around the world are increasingly investing in artificial intelligence (AI) to automate military tasks that traditionally required human involvement. Despite growing interest in AI-enabled systems, relatively little research explores whether and how AI affects military decision-making. Yet, national security practitioners may perceive the judgments of and actions taken by algorithms differently than those of humans. This variation may subsequently affect decisions on the use of force. Using two original survey experiments fielded on a sample of US national security experts, we find that AI use by both friendly and rival forces affects decision-making during interstate crises. National security experts are less likely to take military action when AI is used to analyze intelligence than when humans conduct the analysis. Experts also viewed an accident involving a rival's AI-enabled weapon that kills American troops as more deserving of retaliation than an accident involving only human operators, suggesting that national security practitioners are less forgiving of errant AI systems than of similarly erring humans. Our findings suggest emerging technologies such as AI can affect decisionmakers' perceptions in ways that shape political outcomes. Even in a world of algorithms, human decisions will still have important consequences for international security. [ABSTRACT FROM AUTHOR]

The rapid growth of artificial intelligence (AI) in radiology has led to Food and Drug Administration clearance of more than 20 AI algorithms for breast imaging. The steps involved in the clinical implementation of an AI product include identifying all stakeholders, selecting the appropriate product to purchase, evaluating it with a local data set, integrating it into the workflow, and monitoring its performance over time. Despite the potential benefits of improved quality and increased efficiency with AI, several barriers, such as high costs and liability concerns, may limit its widespread implementation. This article lists currently available AI products for breast imaging, describes the key elements of clinical implementation, and discusses barriers to clinical implementation. [ABSTRACT FROM AUTHOR]

Motivation The recent emergence of cloud laboratories—collections of automated wet-lab instruments that are accessed remotely, presents new opportunities to apply Artificial Intelligence and Machine Learning in scientific research. Among these is the challenge of automating the process of optimizing experimental protocols to maximize data quality. Results We introduce a new deterministic algorithm, called P a R allel O ptimiza T i O n for C l O ud L aboratories (PROTOCOL), that improves experimental protocols via asynchronous, parallel Bayesian optimization. The algorithm achieves exponential convergence with respect to simple regret. We demonstrate PROTOCOL in both simulated and real-world cloud labs. In the simulated lab, it outperforms alternative approaches to Bayesian optimization in terms of its ability to find optimal configurations, and the number of experiments required to find the optimum. In the real-world lab, the algorithm makes progress toward the optimal setting. Data availability and implementation PROTOCOL is available as both a stand-alone Python library, and as part of a R Shiny application at https://github.com/clangmead/PROTOCOL. Data are available at the same repository. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]