Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study
رانندگان ، موانع و ملاحظات اجتماعی برای پذیرش هوش مصنوعی در مشاغل و مدیریت: یک مطالعه عالی-2020
The number of academic papers in the area of Artificial Intelligence (AI) and its applications across business and management domains has risen significantly in the last decade, and that rise has been followed by an increase in the number of systematic literature reviews. The aim of this study is to provide an overview of existing systematic reviews in this growing area of research and to synthesise their findings related to enablers, barriers and social implications of the AI adoption in business and management. The methodology used for this tertiary study is based on Kitchenham and Charter’s guidelines , resulting in a selection of 30 reviews published between 2005 and 2019 which are reporting results of 2,021 primary studies. These reviews cover the AI adoption across various business sectors (healthcare, information technology, energy, agriculture, apparel industry, engineering, smart cities, tourism and transport), management and business functions (HR, customer services, supply chain, health and safety, project management, decisionsupport, systems management and technology acceptance). While the drivers for the AI adoption in these areas are mainly economic, the barriers are related to the technical aspects (e.g. availability of data, reusability of models) as well as the social considerations such as, increased dependence on non-humans, job security, lack of knowledge, safety, trust and lack of multiple stakeholders’ perspectives. Very few reviews outside of the healthcare management domain consider human, organisational and wider societal factors and implications of the AI adoption. Most of the selected reviews are recommending an increased focus on social aspects of AI, in addition to more rigorous evaluation, use of hybrid approaches (AI and non-AI) and multidisciplinary approaches to AI design and evaluation. Furthermore, this study found that there is a lack of systematic reviews in some of the AI early adopter sectors such as financial industry and retail and that the existing systematic reviews are not focusing enough on human, organisational or societal implications of the AI adoption in their research objectives.
Keywords: artificial intelligence | business | machine learning | management | systematic literature review | tertiary study
ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial
غربالگری هدایت شده با هوش مصنوعی ECG برای کسر کم دفع (EAGLE): منطق و طراحی یک آزمایش تصادفی خوشه عملی-2020
Background A deep learning algorithm to detect low ejection fraction (EF) using routine 12-lead electrocardiogram (ECG) has recently been developed and validated. The algorithm was incorporated into the electronic health record (EHR) to automatically screen for low EF, encouraging clinicians to obtain a confirmatory transthoracic echocardiogram (TTE) for previously undiagnosed patients, thereby facilitating early diagnosis and treatment. Objectives To prospectively evaluate a novel artificial intelligence (AI) screening tool for detecting low EF in primary care practices. Design The EAGLE trial is a pragmatic two-arm cluster randomized trial (NCT04000087) that will randomize N100 clinical teams (i.e., clusters) to either intervention (access to the new AI screening tool) or control (usual care) at 48 primary care practices across Minnesota and Wisconsin. The trial is expected to involve approximately 400 clinicians and 20,000 patients. The primary endpoint is newly discovered EF ≤50%. Eligible patients will include adults who undergo ECG for any reason and have not been previously diagnosed with low EF. Data will be pulled from the EHR, and no contact will be made with patients. A positive deviance qualitative study and a post-implementation survey will be conducted among select clinicians to identify facilitators and barriers to using the new screening report. Summary This trial will examine the effectiveness of the AI-enabled ECG for detection of asymptomatic low EF in routine primary care practices and will be among the first to prospectively evaluate the value of AI in real-world practice. Its findings will inform future implementation strategies for the translation of other AI-enabled algorithms. (Am Heart J 2020;219:31-6.)
Lessons Learned About Autonomous AI: Finding a Safe, Efficacious, and Ethical Path Through the Development Process
درسهایی که درباره هوش مصنوعی مستقل آموخته اند: یافتن راهی ایمن ، کارآمد و اخلاقی از طریق فرایند توسعه-2020
Artificial intelligence (AI) describes systems capable of making decisions of high cognitive complexity; autonomous AI systems in healthcare are AI systems that make clinical decisions without human oversight. Such rigorously validated medical diagnostic AI systems hold great promise for improving access to care, increasing accuracy, and lowering cost, while enabling specialist physicians to provide the greatest value by managing and treating patients whose outcomes can be improved. Ensuring that autonomous AI provides these benefits requires evaluation of the autonomous AI’s effect on patient outcome, design, validation, data usage, and accountability, from a bioethics and accountability perspective. We performed a literature review of bioethical principles for AI, and derived evaluation rules for autonomous AI, grounded in bioethical principles. The rules include patient outcome, validation, reference standard, design, data usage, and accountability for medical liability. Application of the rules explains successful US Food and Drug Administration (FDA) de novo authorization of an example, the first autonomous point-of-care diabetic retinopathy examination de novo authorized by the FDA, after a preregistered clinical trial. Physicians need to become competent in understanding the potential risks and benefits of autonomous AI, and understand its design, safety, efficacy and equity, validation, and liability, as well as how its data were obtained. The autonomous AI evaluation rules introduced here can help physicians understand limitations and risks as well as the potential benefits of autonomous AI for their patients. (Am J Ophthalmol 2020;214:134–142.
Determinants of Cone and Rod Functions in Geographic Atrophy: AI-Based Structure- Function Correlation
عوامل تعیین کننده عملکردهای مخروطی و میله ای در آتروفی جغرافیایی: همبستگی عملکردی مبتنی بر هوش مصنوعی-2020
PURPOSE: To investigate the association between retinal microstructure and cone and rod function in geographic atrophy (GA) secondary to age-related macular degeneration (AMD) by using artificial intelligence (AI) algorithms. DESIGN: Prospective, observational case series. METHODS: A total of 41 eyes of 41 patients (75.8 ± 8.4 years old; 22 females) from a tertiary referral hospital were included. Mesopic, dark-adapted (DA) cyan and red sensitivities were assessed by using funduscontrolled perimetry (‘‘microperimetry’’); and retinal microstructure was assessed by using spectral-domain optical- coherence-tomography (SD-OCT), fundus autofluorescence (FAF), and near-infrared-reflectance (IR) imaging. Layer thicknesses and intensities and FAF and IR intensities were extracted for each test point. The cross-validated mean absolute error (MAE) was evaluated for random forest-based predictions of retinal sensitivity with and without patient-specific training data and percentage of increased mean-squared error (%IncMSE) as measurement of feature importance. RESULTS: Retinal sensitivity was predicted with a MAE of 4.64 dB for mesopic, 4.89 dB for DA cyan, and 4.40 dB for DA red testing in the absence of patient-specific data. Partial addition of patient-specific.
Combining gaze and AI planning for online human intention recognition
تلفیق برنامه ریزی نگاه و هوش مصنوعی برای تشخیص آنلاین نیت انسان-2020
Intention recognition is the process of using behavioural cues, such as deliberative actions, eye gaze, and gestures, to infer an agent’s goals or future behaviour. In artificial intelligence, one approach for intention recognition is to use a model of possible behaviour to rate intentions as more likely if they are a better ‘fit’ to actions observed so far. In this paper, we draw from literature linking gaze and visual attention, and we propose a novel model of online human intention recognition that combines gaze and model-based AI planning to build probability distributions over a set of possible intentions. In human-behavioural experiments (n =40) involving a multi-player board game, we demonstrate that adding gaze-based priors to model-based intention recognition improved the accuracy of intention recognition by 22% (p <0.05), determined those intentions ≈90 seconds earlier (p <0.05), and at no additional computational cost. We also demonstrate that, when evaluated in the presence of semi-rational or deceptive gaze behaviours, the proposed model is significantly more accurate (9% improvement) (p <0.05) compared to a model-based or gaze only approaches. Our results indicate that the proposed model could be used to design novel human-agent interactions in cases when we are unsure whether a person is honest, deceitful, or semi-rational.
Keywords: Intention recognition | Gaze | Planning
The Hanabi challenge: A new frontier for AI research
چالش Hanabi : مرز جدیدی برای تحقیقات هوش مصنوعی-2020
From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made dramatic advances with artificial agents reaching superhuman performance in challenge domains like Go, Atari, and some variants of poker. As with their predecessors of chess, checkers, and backgammon, these game domains have driven research by providing sophisticated yet well-defined challenges for artificial intelligence practitioners. We continue this tradition by proposing the game of Hanabi as a new challenge domain with novel problems that arise from its combination of purely cooperative gameplay with two to five players and imperfect information. In particular, we argue that Hanabi elevates reasoning about the beliefs and intentions of other agents to the foreground. We believe developing novel techniques for such theory of mind reasoning will not only be crucial for success in Hanabi, but also in broader collaborative efforts, especially those with human partners. To facilitate future research, we introduce the open-source Hanabi Learning Environment, propose an experimental framework for the research community to evaluate algorithmic advances, and assess the performance of current state-of-the-art techniques.
Keywords: Multi-agent learning | Challenge paper | Reinforcement learning | Games | Theory of mind | Communication | Imperfect information | Cooperative
AI-enabled recruiting: What is it and how should a manager use it?
استخدام با هوش مصنوعی: چه چیزیست و یک مدیر چگونه باید از آن استفاده کند؟-2020
AI-enabled recruiting systems have evolved from nice to talk about to necessary to utilize. In this article, we outline the reasons underlying this development. First, as competitive advantages have shifted from tangible to intangible assets, human capital has transitioned from supporting cast to a starring role. Second, as digitalization has redesigned both the business and social landscapes, digital recruiting of human capital has moved from the periphery to center stage. Third, recent and near-future advances in AI-enabled recruiting have improved recruiting efficiency to the point that managers ignore them or procrastinate their utilization at their own peril. In addition to explaining the forces that have pushed AI-enabled recruiting systems from nice to necessary, we outline the key strategic steps managers need to take in order to capture its main benefits.
KEYWORDS : AI-enabled recruiting | Artificial intelligence | Digital recruiting | technology | Human resources
From data to action: How marketers can leverage AI
از داده به عمل: بازاریاب ها چگونه می توانند از هوش مصنوعی استفاده کنند-2020
Artificial intelligence (AI) is at the forefront of a revolution in business and society. AI affords companies a host of ways to better understand, predict, and engage customers. Within marketing, AI’s adoption is increasing year-on-year and in varied contexts, from providing service assistance during customer interactions to assisting in the identification of optimal promotions. But just as questions about AI remain with regard to job automation, ethics, and corporate responsibility, the marketing domain faces its own concerns about AI. With this article, we seek to consolidate the growing body of knowledge about AI in marketing. We explain how AI can enhance the marketing function across nine stages of the marketing planning process. We also provide examples of current applications of AI in marketing.
KEYWORDS : Artificial intelligence | Machine learning | Marketing function | Marketing mix | Consumer engagement | Customer experience | Customer journey
Collaborative AI and Laboratory Medicine integration in precision cardiovascular medicine
یکپارچه سازی هوش مصنوعی و داروی آزمایشگاهی در پزشکی دقیق قلب و عروق-2020
Artificial Intelligence (AI) is a broad term that combines computation with sophisticated mathematical models and in turn allows the development of complex algorithms which are capable to simulate human intelligence such as problem solving and learning. It is devised to promote a significant paradigm shift in the most diverse areas of medical knowledge. On the other hand, Cardiology is a vast field dealing with diseases relating to the heart, the circulatory system, and includes coronary heart disease, cerebrovascular disease, rheumatic heart disease and other conditions. AI has emerged as a promising tool in cardiovascular medicine which is aimed in augmenting the effectiveness of the cardiologist and to extend better quality to patients. It has the ability to support decision‑making and improve diagnostic and prognostic performance. Attempt has also been made to explore novel genotypes and phenotypes in existing cardiovascular diseases, improve the quality of patient care, to enable cost-effectiveness with reduce readmission and mortality rates. Our review addresses the integration of AI and laboratory medicine as an accelerator of personalization care associated with the precision and the need of value creation services in cardiovascular medicine.
Keywords: Artificial intelligence | Cardiology | Laboratory | Biomarkers | Data | Machine learning | Personalized
AI-based detection of erythema migrans and disambiguation against other skin lesions
تشخیص اریتم مهاجر بر اساس هوش مصنوعی و ابهام زدایی در برابر سایر ضایعات پوستی-2020
This study examines the use of AI methods and deep learning (DL) for prescreening skin lesions and detecting the characteristic erythema migrans rash of acute Lyme disease. Accurate identification of erythema migrans allows for early diagnosis and treatment, which avoids the potential for later neurologic, rheumatologic, and cardiac complications of Lyme disease. We develop and test several deep learning models for detecting erythema migrans versus several other clinically relevant skin conditions, including cellulitis, tinea corporis, herpes zoster, erythema multiforme, lesions due to tick bites and insect bites, as well as non-pathogenic normal skin. We consider a set of clinically-relevant binary and multiclass classification problems of increasing complexity. We train the DL models on a combination of publicly available images and test on public as well as images obtained in the clinical setting. We report performance metrics that measure agreement with a gold standard, as well as a receiver operating characteristic curve and associated area under the curve. On public images, we find that the DL system has an accuracy ranging from 71.58% (and 95% error margin equal to 3.77%) for an 8-class problem of EM versus 7 other classes including other skin pathologies, insect bites and normal skin, to 94.23% (3.66%) for a binary problem of EM vs. non-pathological skin. On clinical images of affected individuals, the DL system has a sensitivity of 88.55% (2.39%). These results suggest that a DL system can help in prescreening and referring individuals to physicians for earlier diagnosis and treatment, in the presence of clinically relevant confusers, thereby reducing further complications and morbidity.