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A survey experiment relying on an information prime to test whether salient awareness of the availability of AI for work tasks affects public servants' competence development motivation, concerns, and assessments of organizational support.

The integration of advanced technologies such as Artificial Intelligence (AI), encompassing machine learning (ML), deep learning, and neural networks, holds significant promise in revolutionizing patient care within outpatient primary clinics and ambulatory care settings. While the exploration of these technologies' implications for healthcare delivery is ongoing, several studies have delved into their potential benefits. AI-driven algorithms can analyze vast amounts of patient data, including electronic health records (EHRs), diagnostic images, and wearable device data, to assist healthcare providers in making more accurate diagnoses and treatment decisions. ML algorithms can predict patient outcomes, identify patterns in disease progression, and personalize treatment plans based on individual patient characteristics and preferences. Deep learning techniques excel in image recognition tasks, aiding in the interpretation of medical images such as X-rays, MRIs, and CT scans. Furthermore, AI-powered clinical decision support systems can augment healthcare professionals' decision-making processes by providing real-time recommendations and alerts regarding potential diagnoses, medication interactions, and treatment options. Despite these advancements, challenges remain in integrating AI technologies into outpatient primary clinics and ambulatory care settings. Issues such as data privacy and security, algorithm bias, interoperability of AI systems with existing healthcare infrastructure, and provider acceptance and adoption need to be addressed to realize the full potential of AI in improving patient care efficiency and effectiveness. In summary, while there has been examination and exploration of the integration of advanced technologies like AI, ML, deep learning, and neural networks in outpatient primary clinics and ambulatory care settings, further research and implementation efforts are needed to fully leverage their benefits and address associated challenges.

In this paper we are interested in how government service delivery will be assessed by clients based on varying applications of AI during program implementation. Hypotheses H1: Government service delivery will be assessed more favorably when AI is used during the service delivery process H2: Government service delivery will be assessed more favorable when AI is used to reduce administrative burdens on clients. (Delivery complexity cue) H2a: Using AI to reduce burdens will have an even stronger positive effect on favorable assessment when government program is perceived as burdensome. H3: Government service delivery will be assessed more favorable when AI is used to provide services that is responsive to applicants’ specific needs. (Delivery agility/adaptability cue) H4: Government service delivery will be assessed more favorable when AI is used to eliminate fraud, misuse, or waste. (Delivery economy cue) H5: citizens assessment of AI-Enabled government service delivery will be more positive when their state government has enacted related AI legislation

This is the registration of scoping review protocol titled "Potential contributions of artificial intelligence in perioperative nursing training: A scoping review". This protocol follows the methodology proposed by Joanna Briggs Institute for scoping reviews. To the best of the authors knowledge, no other protocol within this field has been made available for the general public

The use of artificial intelligence (AI) in the diagnosis of temporomandibular disorder (TMD) has emerged as a promising approach. AI algorithms have been successfully employed in identifying several subtypes of TMD, encompassing masticatory muscle disorders, joint disorders, internal temporomandibular joint (TMJ) derangement, and disc perforation. For the advancement and improvement of AIs, it becomes imperative to achieve a deep understanding of the specific areas in which they are being specialized, identifying existing gaps, and anticipating the strategies needed to deal with future datasets.

Cancers of the oral cavity are one of the most prevalent cancers worldwide. With highest incidence in developing countries in Asia, they have shown huge variation in geographical distribution. In Taiwan, oral cancer is the 6th most common cancer overall and the 4th most common cancer among men. Oral cavity cancer often results in impaired functions and esthetics like difficulty in breathing, swallowing, mastication, speech along with disfigurement of the face. Advanced stage oral cancer is associated with high mortality rates and poses a serious threat to public health. Around 85% of these malignant lesions are diagnosed as oral squamous cell carcinoma (OSCC) and this diagnosis is confirmed through tissue biopsy. Although currently considered as gold standard, histopathological classification and tissue biopsies (surgical biopsy, punch biopsy, lymph node biopsy, brush biopsy, and needle aspiration biopsy) are invasive, time consuming and cannot be repeated frequently. Further, prediction of dysplastic oral lesions and OSCC only through visual inspection is challenging and demands significant training and expertise. It is therefore crucial to explore alternative diagnostic/screening tools like Artificial Intelligence (AI) and Machine Learning (ML) tools for image classification. Currently, deep learning (DL) is a dominant machine learning (ML) technique establishing new capability standards for image classification in a variety of domains including medicine. DL has shown promising results in several image-based medical screening and diagnostic applications. It is important to learn the progress and development of work in the literature on using ML especially DL techniques for automatic oral cancer image analysis/classification. To help understand what has been done, identify the challenges and opportunities, address its research gap, and improve its status of the art, we propose to conduct a scoping review on this topic. Study Objective: This scoping review aims at mapping the studies and synthesizing the evidence on the artificial intelligence and/or machine learning techniques available for the classification of intraoral images to identify oral cancers or precancers. Furthermore, the review will also explore and compare the performance of the models/techniques when used for classification of oral cancer and/or precancer and how they aid in diagnosis made visually. The research gap will be identified and will be used to provide direction for future studies in this area.

Artificial Intelligence (AI) has been employed in the medical field for diagnosing various diseases. Moreover, the use of AI has significantly impacted pediatrics by identifying sepsis subtypes, diagnosing pulmonary hypertension and neonatal jaundice, and detecting brain changes in neuroimaging. On the other hand, breastfeeding rates are low, increasing the risk of illness and mortality in children. The use of AI could contribute to improving health indicators and enhancing knowledge about human milk. The aim of this project is to identify and synthesize the currently available information on the application of artificial intelligence in the analysis of human milk and breastfeeding.

We investigate detection, classification, and prognostication of osteosarcoma using artificial intelligence.

A.I, what do we think about it? IDK but I'm sure we make zero sense. Let us know what you think @beeroverchatter 

The objective is to develop a system that will allow a person not necessarily skilled in the art of programming robots to quickly and naturally create the necessary data and commands to enable a robot to perform a desired task. The system will use a menu driven graphical user interface. This interface will allow the user to input data to select objects to be moved. There will be an imbedded expert system to process the knowledge about objects and the robot to determine how they are to be moved. There will be automatic path planning to avoid obstacles in the work space and to create a near optimum path. The system will contain the software to generate the required robot instructions.

Artificial intelligence is shaping healthcare in Canada and around the world. The role of AI in delivering care will evolve and, indeed, grow. As a series of tools, it offers opportunities for patients and practitioners; yet, as with any technology, it comes with risks. As with any tool, AI will only be as good as those who create and operate it. Given who we are - and our track record - that ought to give us pause.Understanding, interrogating, and mobilizing new technologies requires care, diligence, and diversity. When it comes to equitable, accessible healthcare, we require heaps and heaps of such considerations. So, in this episode we ask: What does artificial intelligence mean for the future of equitable, accessible healthcare? On this episode of Open to Debate, David Moscrop talks with Dr. Rim Khazal, an expert on AI policy who holds a PhD in Neuroscience from Carleton University.

The project aims to synthesise the available literature on how artificial intelligence can support interprofessional collaborative practice in healthcare settings.

LUCACI FLORENTIN - BLUE HEART - DISASTER ZONE ARTIFICIAL INTELLIGENCE

Ben Goertzel is one of the most interesting minds in the artificial intelligence community. He is the founder of SingularityNET, designer of OpenCog AI framework, formerly a director of the Machine Intelligence Research Institute, Chief Scientist of Hanson Robotics, the company that created the Sophia Robot. He has been a central figure in the AGI community for many years, including in the Conference on Artificial General Intelligence. Support this podcast by supporting these sponsors: - Jordan Harbinger Show: https://jordanharbinger.com/lex/ - MasterClass: https://masterclass.com/lex This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this

Brian Thomas, Agency Data Scientist at NASA Headquarters, discusses artificial intelligence.

Darrell West is vice president of the Brookings Institution and director of Governance Studies at Brookings. John Allen is the president of the Brookings Institution and a retired U.S. Marine Corps four-star general. Together, they are the authors of the book, "Turning Point: Policymaking in the Era of Artificial Intelligence," a broad look at the impact that artificial intelligence systems are likely to have on everything from the military, to health care, to vehicles and transportation, and to international great power competition. They joined Benjamin Wittes to discuss the book and the question of how we should govern AI systems. What makes for ethical uses of AI? What makes it scary? What are the anxieties that people have about artificial intelligence and to what extent are the fears legitimate?Support this show http://supporter.acast.com/lawfare. See acast.com/privacy for privacy and opt-out information.

Perma.cc archive of https://www.americanbar.org/groups/labor_law/publications/labor_employment_law_news/spring-2022/ai-in-the-workplace/ created on 2022-10-02 17:40:26.586211+00:00.

We mark the 20th anniversary of the Iraq War by revisiting some of the classic clips from our cowed and deferential news media circa 2003. PLUS: What is artificial intelligence and what does it mean for the future? Pulitzer-winning columnist Thomas Friedman doesn't know, but that didn't stop him from writing about it.

Perma.cc archive of https://www.gibsondunn.com/2021-artificial-intelligence-and-automated-systems-annual-legal-review/ created on 2022-04-06 16:44:38.350716+00:00.

Perma.cc archive of https://techgdpr.com/blog/artificial-intelligence-right-to-information-explanation/ created on 2020-09-27 19:30:43+00:00.

This project investigates how exposure to information about the capabilities of AI, specifically large language models (LLMs), influences preferences for regulating AI tools among white collar professionals. It explores the connection between pessimism about future income prospects and support for AI regulation, contrasting this with attitudes towards other automation tools. The study expands on existing research by examining a wider variety of policy outcomes relevant to LLMs and considering both personal financial concerns and sociotropic concerns about AI's impact on the labor market.

The Artificial Intelligence (AI) Toolkit is a computer program for training scientists, engineers, and university students in three soft-computing techniques (fuzzy logic, neural networks, and genetic algorithms) used in artificial-intelligence applications. The program promotes an easily understandable tutorial interface, including an interactive graphical component through which the user can gain hands-on experience in soft-computing techniques applied to realistic example problems. The tutorial provides step-by-step instructions on the workings of soft-computing technology, whereas the hands-on examples allow interaction and reinforcement of the techniques explained throughout the tutorial. In the fuzzy-logic example, a user can interact with a robot and an obstacle course to verify how fuzzy logic is used to command a rover traverse from an arbitrary start to the goal location. For the genetic algorithm example, the problem is to determine the minimum-length path for visiting a user-chosen set of planets in the solar system. For the neural-network example, the problem is to decide, on the basis of input data on physical characteristics, whether a person is a man, woman, or child. The AI Toolkit is compatible with the Windows 95,98, ME, NT 4.0, 2000, and XP operating systems. A computer having a processor speed of at least 300 MHz, and random-access memory of at least 56MB is recommended for optimal performance. The program can be run on a slower computer having less memory, but some functions may not be executed properly.

An integrative review of the scientific production of Brazilian researchers on technologies based on artificial intelligence in nursing is the aim of this protocol. To conduct this study, the following steps will be followed: outlining the problem and object of research, searching the literature, evaluating and critically analyzing the data and presenting the results (Whittemore, Knafl, 2005). Based on the PICo strategy, respectively related to Population (P), Concept (C), Context (C); P (Nursing/Nursing), C (Technologies based on artificial intelligence), C (professionals working in Brazilian institutions), the research question was defined: What technologies based on artificial intelligence are published by nurses in Brazilian institutions? The inclusion criteria defined were Publications (articles published in national and international journals, theses and dissertations), by nurses from Brazilian institutions, in the last 10 years, in the various areas of nursing practice, in Portuguese, English or Spanish.

In recent years, advances in deep learning have transformed the analysis of med- 21 ical imaging, especially in spine oncology. Computed Tomography (CT) imaging is crucial for di- 22 agnosing, planning treatment, and monitoring spinal tumors. This review aims to comprehensively 23 explore the current uses of deep learning tools in CT-based spinal oncology. Additionally, potential 24 clinical applications of AI designed to address common challenges in this field will also be ad- 25 dressed.

The use of Artificial Intelligence and its application in pre, intra and postoperative periods in cochlear implantation. This would provide a critical and prospective input on the use of AI and the future of its applications. Thus, the aim of the scoping review and the question would be: "what is the future of AI application in cochlear implantation?"

Objective: This scoping review aims to identify artificial intelligence methods applied to patient-reported data for prediction of patient outcomes in healthcare. Introduction: Artificial intelligence methods have been used to predict outcomes for patients from many types of data, including diagnostic images, genetic data, and time-series data from wearable sensors or electronic healthcare records. The lack of sufficient evidence of AI methodology applied to potentially equally informative self-reported symptom data is the motivation for this scoping review. Inclusion criteria: Primary research studies reported in English will be included in this review. Considered population will be patients, who self-reported their symptoms in the context of healthcare. The concept analysed in the review will be the methods of Artificial Intelligence used for prediction of patient outcomes. The outcomes can be related to health status, mortality, treatment, healthcare utilisation and self-management of health issues. Methods: The databases used in the proposed scoping review will be Web of Science, IEEE Xplore, ACM Digital Library, Cochrane Central Register of Controlled Trials, Medline and Embase. The search terms will be adapted to each database, addressing the same research question. Following exporting of references, duplicates will be removed, and identified papers will be screened against the inclusion and exclusion criteria. The comparison of methods, evaluation styles, data types and healthcare contexts reported in selected articles will be presented in a narrative and a tabular form.

we aim to present a comprehensive review of the current state-of-the-art applications in robotic radical prostatectomy, encompassing preoperative planning, intraoperative assistance, and postoperative outcomes

In this Fully-Connected episode, Chris and Daniel explore the geopolitics, economics, and power-brokering of artificial intelligence. What does control of AI mean for nations, corporations, and universities? What does control or access to AI mean for conflict and autonomy? The world is changing rapidly, and the rate of change is accelerating. Daniel and Chris look behind the curtain in the halls of power.

In this Fully-Connected episode, Chris and Daniel explore the geopolitics, economics, and power-brokering of artificial intelligence. What does control of AI mean for nations, corporations, and universities? What does control or access to AI mean for conflict and autonomy? The world is changing rapidly, and the rate of change is accelerating. Daniel and Chris look behind the curtain in the halls of power.

To explore the extent of actual use of AI/ ML protocols for diagnosing cancer in prospective (clinical trial or real-world) setting

Perma.cc archive of https://www.orrick.com/en/Insights/2021/11/US-Artificial-Intelligence-Regulation-Takes-Shape created on 2022-04-06 17:54:50.221144+00:00.

Perma.cc archive of https://www.nist.gov/artificial-intelligence/national-artificial-intelligence-advisory-committee-naiac created on 2022-04-06 13:37:38.890679+00:00.

Panelists talked about the impact of artificial intelligence (AI) and automation on jobs. Speakers included authors and educators, Google's chief economist, and a Microsoft AI research specialist. This program was part of the Technology Policy Institute's 2017 Aspen Forum. Sponsor: Technology Policy Institute

The House Financial Services Task Force on Artificial Intelligence (AI) examined the use of AI technology to combat the coronavirus pandemic. Lawmakers and expert panelists also debated privacy concerns with using such technology. This hearing was held virtually due to the on-going pandemic. Sponsor: House Financial Services Committee

National Oceanic and Atmospheric Administration (NOAA) Chief Technology Officer Frank Indiviglio and others discuss the use of artificial intelligence in government agencies.

On today's show, CEO of PORTpass Zak Hussein and Ritesh Kotak, a cybersecurity tech analyst join the show to talk about privacy and security concerns surrounding the private vaccine verification app Portpass. We discuss why there's a need for concrete protections from artificial intelligence threatening human rights with Sarit Mizrahi, a Ph.D. law candidate at the University of Ottawa. Plus, what can the world learn from the Canada-China prisoner swap? We hear from David Webster, an associate professor of history at Bishop's University.Learn more about your ad choices. Visit megaphone.fm/adchoices

Taken from JRE #1919 w/Bret Weinstein: https://open.spotify.com/episode/1fCp0jGnwvxHJVPiXTfOzA?si=eb9360af79b142d7

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U.S. Chief Technology Officer Michael Kratsios talked about artificial intelligence technology and tackling Chinese competition. Mr. Kratsios explained the importance of western countries leading in artificial intelligence development and how the U.S. government works with the private sector to create new technologies. Sponsor: Hudson Institute

This is a cross-sectional survey of awareness and attitudes among the UK public towards the use of AI in diagnostic pathology.

Perma.cc archive of http://news.mit.edu/2019/automating-ai-medical-decisions-0806 created on 2022-02-12 16:12:57.213092+00:00.

Systematic review looking at articles on wireless sensing in the medical field, for the purpose of prognosing, diagnosing, or treating medical conditions, in the past 10 years.

Research project on the public perception of ADM systems for the distribution of vaccines against the Coronavirus

In scholarly publishing, artificial intelligence chatbots (AICs)—advanced conversational programs—are emerging as generative artificial intelligence (AI) tools that can support the peer review process by automating tasks, such as identifying methodological flaws, verifying references, and improving language clarity. These tools hold potential to enhance efficiency, reduce reviewer workload, and address inconsistencies in peer review quality. However, their integration also raises significant challenges, including concerns about reliability, ethical implications, and transparency in decision-making. Peer review, a critical component of scholarly publishing, is already under strain due to increasing demands on reviewers and longstanding issues such as variability in review quality and potential biases. AICs have been proposed as tools to assist and augment the peer review process, for example, by ensuring clarity, completeness, and logical stringency of content. However, little is known about how peer reviewers perceive their role, utility, and potential impact. This study will conduct an international cross-sectional survey to assess peer reviewers’ attitudes and perceptions towards AICs in the peer review process. The survey aims to explore reviewers’ familiarity with AI, perceived benefits and challenges, ethical concerns, and anticipated future roles for AICs in peer review. Findings will provide insights to guide the development of policies and best practices for the ethical and effective integration of AICs in peer review.

If you could talk to Genghis Khan, what questions would you ask him? Could a life-like conversation with a historical figure change your opinion of them? Might you be able to understand their reasons and perspectives better? This study explores the ability of artificial intelligence (AI) to enhance historical empathy (HE) in students by comparing AI-based conversational tools to traditional learning methods. HE is defined best as a process in which individuals can cognitively think about and emotionally connect with historical figures as a way to better understand the context of their lives, decisions, and actions at that time. The importance of developing HE can be underscored by the notion that fostering it is a hearty way to grow an individual’s sense of citizenship and democracy. Using a within-subjects experimental design, participants are randomized to engage with historical figures through an AI-powered application, Hello History, and read about the same figures in printed textbooks. Using a subscale from the Historical Thinking Skills Scale (HTSS) to measure HE, the study hypothesizes that AI-based conversational interactions will yield higher HE scores than traditional textbook-based learning. Results are expected to contribute to the growing discourse on AI use for students in educational settings by advancing cultural shifts within academia that are integral for sharpening individuals’ critical and ethical senses in the use and consumption of AI. In short, the readers of this paper should care to converse with an AI-powered Genghis Khan chat box to sharpen their abilities to understand others, the world around them, the world that came before them, and arguably most importantly, to help foster a more inclusive world for tomorrow.

History Professors Matthew Jones (Princeton), Janet Abbate (Virginia Tech), Matthew Connelly (Columbia), and Jeffrey Yost (University of Minnesota) discussed the historical development of artificial intelligence. The American Historical Association hosted this discussion as part of a Congressional briefing series. Sponsor: American Historical Association

Current and former government officials spoke at the National Security Commission on Artificial Intelligence conference in Washington, DC. During this portion, former Secretary of State Henry Kissinger shared his thoughts on the emergence of artificial intelligence and the role it will play in the world, saying that "A.I. will be the philosophical challenge of the future." Sponsor: National Security Commission on Artificial Intelligence

President Biden delivered remarks and signed an Executive Order regarding the regulation and privacy standards of artificial intelligence (AI) during an event with government and business leaders hosted at the White House. Among the requirements of The Executive Order are the use of Defense Production Act to require transparency in safety testing of AI systems, increased safety standards, consumer privacy, and civil rights and equity guidelines. At the top of his remarks, the president applauded the United Auto Workers and the Big Three automakers for reaching, what he called, a "historic agreement" in agreeing to a new contract for auto workers. Also delivering remarks was Vice President Kamala Harris. Sponsor: White House

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