ChatGPT is a generative language model tool launched by OpenAI on November 30, 2022, enabling the public to converse with machine broad range of topics. In January 2023, reached over 100 million users, making it fastest-growing consumer application date. This interview part 2 larger ChatGPT. It provides snapshot current capabilities and illustrates vast potential for medical education, research, practice but also hints at problems limitations. this conversation Gunther Eysenbach, founder publisher JMIR Publications, generated some ideas how use chatbots in education. illustrated its generate virtual patient simulation quizzes students; critiqued simulated doctor-patient communication attempts summarize research article (which turned out be fabricated); commented methods detect machine-generated text ensure academic integrity; curriculum health professionals learn about artificial intelligence (AI); helped draft call papers new theme issue Medical Education The highlighted importance proper "prompting." Although generator does make occasional mistakes, admits these when challenged. well-known disturbing tendency large models hallucinate became evident fabricated references. glimpse into limitations future AI-supported Due impact technology launching e-collection issue. initial was entirely ChatGPT, will edited human guest editors

Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis eye diseases systemic disorders¹. However, development AI models requires substantial annotation are usually task-specific with limited generalizability to different clinical applications². Here, we present RETFound, a foundation model that learns generalizable representations from unlabelled provides basis label-efficient adaptation several applications. Specifically, RETFound is trained on 1.6 million by means self-supervised learning then adapted disease detection tasks explicit labels. We show consistently outperforms comparison prognosis sight-threatening diseases, as well incident prediction complex disorders such heart failure myocardial infarction fewer labelled data. solution improve performance alleviate workload experts enable broad applications imaging.

In recent years, artificial intelligence (AI) has become an emerging trend in different fields: science, business, medicine, automotive and education. AI also reached marketing. The aim of the paper is to research how deeply applied marketing what implications there are for practitioners. Authors stated two questions - which areas used delivers managers. To answer those questions, authors conducted on secondary data with examples purpose. analysis gathered shows that widely introduced into field, though applications at operational level. This may be effect careful implementation new technology, still level experimenting it. uncertainty outcome affect caution putting these innovations practice as well. Gathered proved influences all aspects mix impacting both consumer value delivery well organization management. especially ideas about implementing marketing, designing incorporate skills team required by technology.

Artificial Intelligence (AI) plays an increasingly important role in improving HCI and user experience. Yet many challenges persist designing innovating valuable human-AI interactions. For example, AI systems can make unpredictable errors, these errors damage UX even lead to undesired societal impact. However, routinely grapples with complex technologies mitigates their unintended consequences. What makes different? interaction appear particularly difficult design? This paper investigates questions. We synthesize prior research, our own design research experience, observations when teaching interaction. identify two sources of AI's distinctive challenges: 1) uncertainty surrounding capabilities, 2) output complexity, spanning from simple adaptive complex. four levels systems. On each level, designers encounter a different subset the challenges. demonstrate how findings reveal new insights for designers, researchers, tool makers productively addressing going forward.

At the recent international negotiations on climate change in Dubai 2023, potential for artificial intelligence (AI) to play a critical role addressing challenges was recognized through initiative “AI Climate Action (#AI4ClimateAction).” Launched by Technology Mechanism—established under United Nations Framework Convention Change (UNFCCC)—this is focused accelerating implementation of Paris Agreement with focus ensuring that use responsible, and benefits are shared equitably. While exploring promising applications like integrating AI improve early warning systems enhancing agricultural yields, there also need minimize carbon footprint applications. More information can be found at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://unfccc.int/ttclear/artificial_intelligence</uri>

Artificial intelligence is likely to perform several roles currently performed by humans, and the adoption of artificial intelligence-based medicine in gastroenterology practice expected near future. Medical image-based diagnoses, such as pathology, radiology, endoscopy, are be first medical field affected intelligence. A convolutional neural network, a kind deep-learning method with multilayer perceptrons designed use minimal preprocessing, was recently reported being highly beneficial including esophagogastroduodenoscopy, colonoscopy, capsule endoscopy. network-based diagnostic program challenged recognize anatomical locations esophagogastroduodenoscopy images, <i>Helicobacter pylori</i> infection, gastric cancer for esophagogastroduodenoscopy; detect classify colorectal polyps; celiac disease hookworm; small intestine motility characterization endoscopy images. help endoscopists provide more accurate diagnosis automatically detecting classifying lesions; therefore, it essential that focus on this novel technology. In review, we describe effects special automatic diagnosis, based endoscopic findings.

Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep or recurrent drive this area. Numerous applications property activity predictions like physicochemical and ADMET properties have recently appeared underpin the strength of technology quantitative structure-property relationships (QSPR) structure-activity (QSAR). intelligence de novo design drives generation meaningful new biologically active molecules towards desired properties. Several examples establish field. Combination with synthesis planning ease is feasible more automated discovery by computers expected near future.

Despite the great media attention for artificial intelligence (AI), many health care professionals term and functioning of AI remain a "black box," leading to exaggerated expectations on one hand unfounded fears other. In this review, we provide conceptual classification brief summary technical fundamentals AI. Possible applications are discussed basis typical work flow in medical imaging, grouped by planning, scanning, interpretation, reporting. The main limitations current techniques, such as issues with interpretability or need large amounts annotated data, briefly addressed. Finally, highlight possible impact nuclear medicine profession, associated challenges and, last but not least, opportunities.

Smart manufacturing (SM) is a term generally applied to the improvement in operations through integration of systems, linking physical and cyber capabilities, taking advantage information including leveraging big data evolution. SM adoption has been occurring unevenly across industries, thus there an opportunity look other industries determine solution roadmap paths for such as biochemistry or biology. The evolution affords managing significantly larger amounts acting on it with analytics improved diagnostics prognostics. approaches can be defined terms dimensions understand their requirements technology gaps. semiconductor industry by improving existing capabilities fault detection, supporting new predictive maintenance. For most these capabilities: (1) quality important factor delivering high solutions; (2) incorporating subject matter expertise often required realizing effective on-line solutions. In future, environment smart concepts digital twin will further enable analytics; however, anticipated that need design remain.

In the post-pandemic era, uncertain global market and rising social-environmental issues drive organizations to adapt their supply chain strategies more dynamic, flexible models, leveraging advanced technologies like AI, big data analytics, decision support systems. This review paper aims examine current research on AI-integrated in sustainable management (SSCM) inform future directions. We adopted bibliometric text analysis, targeting 170 articles published between 2004 2023 from Scopus database following PRISMA (Preferred Reporting Items for Systematic Reviews Meta-Analyses) protocol. confirm that have demonstrated capability enable SSCM across various sectors. generated ten topics using Latent Dirichlet Allocation (LDA) method proposed 20 propositions. The results show processes primarily address sustainability, focusing environmental economic issues. However, there is still a technological gap tackling social working conditions fair dealing. Thus, we dynamic framework of AI help researchers practitioners synthesize optimize models

Artificial intelligence (AI), which includes machine learning and deep has been introduced to nursing care in recent years. The present study reviews the following topics: concepts of AI, learning, learning; examples AI-based research; necessity education on AI schools; areas where is useful. refers an intelligent system consisting not a human, but machine. Machine computers' ability learn without being explicitly programmed. Deep subset that uses artificial neural networks multiple hidden layers. It suggested educational curriculum should include big data, concept algorithms models model coding practice. standard be organized by society. An example area useful prenatal interventions based pregnant women's records prediction risk delivery according age. Nurses able cope with rapidly developing environment influenced understand how apply their field. time for Korean nurses take steps become familiar research, education,

Abstract The study presents the results regarding attitudes of students from humanities and technical specializations in Timișoara towards emergence development artificial intelligence (AI). emphasis was on most likely consequences future, especially negative that its would entail. method used for data collection sociological survey information gathering tool questionnaire. It applied to a total 929 people, ensuring sample representativity margin ± 3%. analysis reveals participants predict due AI, interpersonal relationships will be negatively affected, there fewer jobs, economic crises emerge, it make intelligent weapons, increase military conflicts, take control humanity and, last but not least, destroy mankind. revealed differences responses depending type specialization (humanities or technical) gender respondents.

This article begins by asking: "Why are robots not becoming domestic products?" In addressing this question the author borrows from science of ecology and biological concepts evolution domestication to make an analogy between shift habitats that occurs when organism successfully goes through process artificial selection (from natural domestic). addition, paper explores transition emerging technology makes coming out laboratory a suitable product for use, concluding majority proposed essentially maladapted everyday life.

Ethics approval is not required because all data for this study have been previously published. The findings of review will be published in a peer-reviewed journal and presented at academic conferences. Importantly, the scoping widely to decision-makers, health system administrators, healthcare providers, patients family/caregivers as part an implementation AI-based CDS treatment coronary artery disease.

When cognitive computing enabled smart computers are growing in our daily lives, there not many studies that explain how people interact and utilize these solutions, the impact of machines to people’s performance do things. In this paper, a theoretical framework for boosting using assistants (CAs) was developed explained data analysis from 15 interviews. The results show interaction with CAs enhance their levels cognition intelligence help them capabilities. Enhanced capabilities performance.

Data-driven science and its corollaries in machine learning the wider field of artificial intelligence have potential to drive important changes medicine. However, medicine is not a like any other: It deeply tightly bound with large wide network legal, ethical, regulatory, economical, societal dependencies. As consequence, scientific technological progresses handling information further processing cross-linking for decision support predictive systems must be accompanied by parallel global environment, numerous stakeholders, including citizen society. What can seen at first glance as barrier mechanism slowing down progression data must, however, considered an asset. Only adoption transform big into effective breakthroughs health This requires society, scientists citizens, progress together.

Journal Article False Responses From Artificial Intelligence Models Are Not Hallucinations Get access Søren Dinesen Østergaard, Østergaard Department of Clinical Medicine, Aarhus University, Aarhus, DenmarkDepartment Affective Disorders, University Hospital - Psychiatry, Denmark To whom correspondence should be addressed; D. – Palle Juul-Jensens Boulevard 175, 8200 N. tel: 45 61282753. e-mail: soeoes@rm.dk Search for other works by this author on: Oxford Academic PubMed Google Scholar Kristoffer Laigaard Nielbo Culture and Society, Center Humanities Computing, Schizophrenia Bulletin, Volume 49, Issue 5, September 2023, Pages 1105–1107, https://doi.org/10.1093/schbul/sbad068 Published: 23 May 2023

The ability to accurately predict the mechanical properties of metals is essential for their correct use in design structures and components. This even more important presence materials, such as metal cast alloys, whose can vary significantly relation constituent elements, microstructures, process parameters or treatments. study shows how a machine learning approach, based on pattern recognition analysis experimental data, able offer acceptable precision predictions with respect main metals, case ductile iron compact graphite iron. metallographic properties, graphite, ferrite perlite content, extrapolated through macro indicators from micrographs by image analysis, are used inputs algorithms, while yield strength, ultimate strain Young’s modulus, derived output. In particular, 3 different algorithms trained starting dataset 20–30 data each material results high accuracy, often better than other predictive techniques. Concerns regarding applicability these techniques product/process quality control also discussed.

Purpose The purpose of this paper is to raise awareness on the need for a more comprehensive approach interdependence between artificial intelligence (AI) and environmental sustainability. It provides an overview existing sustainable AI policy initiatives at national regional level. More precisely, it discusses whether European Union (EU) policies are suitable era or new regulations needed in field. Finally, assesses cross-fertilisation opportunities EU non-EU countries. Design/methodology/approach This study based qualitative analysis applications sustainability AI. Emphasis laid latter, “sustainable by design” proposed, which essence prerequisite transparent, responsible human-centred systems. primarily focuses Findings majority studies focus how use protect environment with very little attention paid design On other hand, EU’s towards closest promoting Several ways have been identified actions can be translated beyond its borders. Research limitations/implications One largest limitations moderate scope. confined as such limited assessment global measures interplay Consequently, did not provide in-depth worldwide that could help better picture possible cooperation areas common grounds. Another limitation aspects accords economic social pillars Social implications With less than 10 years go before reaching development goal deadline, stakeholders understand what being done terms Moreover, given technology still early phase, inspire technologies. Originality/value All strategies published 1 June 2021 were analysed identify extent they prioritise Furthermore, authors also looked aims address from perspective.

This thesis concerns the use of an analogue as aid to a problem solving program. A -working system, advantages it uses, mechanisms required, and interaction ether forms knowledge are described. The program, WHISPER, uses diagram together with procedures for modifying it, situation involving stack arbitrarily shaped rigid bodies. It determines stack's stability predicts motions cf any unstable object by examining situation's diagram. is particularly valuable in detecting discontinuities object's motion. For example, collisions other objects or cliffs might slide over can be 'seen' rather than having inferred from description situation. WHISPER simulated parallel processing 'retina' look at which encoded two-dimensional array. consists fixed number processors operating communicating only their immediate neighbours. WHISPER's retina resembles human some respects. Its resolution decreases away its center. moved fixate on different sections set domain independent features extracted diagrams procedures, called perceptual primitives, execute retina. Example are: symmetry object, similarity two objects, contacts objects. In addition these used 'visualize1 rotation without move directly analogues classified terms categories according whether correspondence exists between behaviour external situation, static configurations those Some reasons effectiveness presented.

Objective: Artificial intelligence (AI) is the new buzzword that trendy in multiple branches of dentistry. The aim this study was to assess perceptions and utilization AI among dental professionals Saudi Arabia. Methods: This a cross-sectional investigating 389 students dentists from 22 cities Arabia using validated self-reported questionnaire. Results: A total 49.4% participants reported knowing what is; 44.5% having basic knowledge principles, 42.2% know uses most common information source social media (66.07%). Out 17 attitude items, 16 were scored above midpoint. 75.0% agreed or strongly will lead major advances In contrast, 49.1% could replace future. There no significant differences by gender region, but interns had significantly higher scores than did dentists. widespread desire take professional courses use (69.7%), some used applications (25.4%) taken an course (18.5%–20.3%). barriers non-availability (73%) lack time (68.9%). Conclusion: Dental have moderate awareness levels high rates good attitudes about However, practice limited. Incorporating curricula crucial due worldwide digital transformation.

Digital Twin technology has been rising in popularity thanks to the of machine learning last decade. As life expectancy people around world is increasing, so focus on physical activity remain healthy especially current times where are staying sedentary while quarantine. This article aims provide a survey field focusing and coaching techniques as they have not explored yet. We also define what Coaching categorize work done far terms objective activity. list common characteristics found articles reviewed concepts such interactivity, privacy security detail future perspectives multimodal interaction standardization, name few, that can guide researchers if choose this field. Finally, we diagram for Ecosystem showing between relevant entities information flow well an idealization ideal team sports' athlete tracking.