Incorrect Information Research Articles

Unmasking DeepFakes Using Machine Learning

Our task objectives to deal with the trouble of Deepfake videos, which can be inflicting fear because of their capacity to unfold fake records and manage human beings. Deepfake veideos appearance so actual that it`s tough to inform them other than actual ones, making them a severe danger. This sensible look can result in sizable damage .We are using Machine Learning Algorithms discover Deepfake videos. This entails the usage of era to differentiate among actual and manipulated content. By growing this, we lessen the effect of incorrect information and manipulation, safeguarding human beings from cappotential damage as a result of deceptive content. Keywords: Deepfake Detection, Machine Learning, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Facial Analysis, Anomaly Detection, Synthetic Media, Image and Video Analysis, Temporal and Spatial Patterns, Training Datasets, Real-time Detection, Visual Content Security

Bayesian-knowledge driven ontologies: A framework for fusion of semantic knowledge under uncertainty and incompleteness.

The modeling of uncertain information is an open problem in ontology research and is a theoretical obstacle to creating a truly semantic web. Currently, ontologies often do not model uncertainty, so stochastic subject matter must either be normalized or rejected entirely. Because uncertainty is omnipresent in the real world, knowledge engineers are often faced with the dilemma of performing prohibitively labor-intensive research or running the risk of rejecting correct information and accepting incorrect information. It would be preferable if ontologies could explicitly model real-world uncertainty and incorporate it into reasoning. We present an ontology framework which is based on a seamless synthesis of description logic and probabilistic semantics. This synthesis is powered by a link between ontology assertions and random variables that allows for automated construction of a probability distribution suitable for inferencing. Furthermore, our approach defines how to represent stochastic, uncertain, or incomplete subject matter. Additionally, this paper describes how to fuse multiple conflicting ontologies into a single knowledge base that can be reasoned with using the methods of both description logic and probabilistic inferencing. This is accomplished by using probabilistic semantics to resolve conflicts between assertions, eliminating the need to delete potentially valid knowledge and perform consistency checks. In our framework, emergent inferences can be made from a fused ontology that were not present in any of the individual ontologies, producing novel insights in a given domain.

Federated Learning with Extremely Noisy Clients via Negative Distillation

Federated learning (FL) has shown remarkable success in cooperatively training deep models, while typically struggling with noisy labels. Advanced works propose to tackle label noise by a re-weighting strategy with a strong assumption, i.e., mild label noise. However, it may be violated in many real-world FL scenarios because of highly contaminated clients, resulting in extreme noise ratios, e.g., >90%. To tackle extremely noisy clients, we study the robustness of the re-weighting strategy, showing a pessimistic conclusion: minimizing the weight of clients trained over noisy data outperforms re-weighting strategies. To leverage models trained on noisy clients, we propose a novel approach, called negative distillation (FedNed). FedNed first identifies noisy clients and employs rather than discards the noisy clients in a knowledge distillation manner. In particular, clients identified as noisy ones are required to train models using noisy labels and pseudo-labels obtained by global models. The model trained on noisy labels serves as a ‘bad teacher’ in knowledge distillation, aiming to decrease the risk of providing incorrect information. Meanwhile, the model trained on pseudo-labels is involved in model aggregation if not identified as a noisy client. Consequently, through pseudo-labeling, FedNed gradually increases the trustworthiness of models trained on noisy clients, while leveraging all clients for model aggregation through negative distillation. To verify the efficacy of FedNed, we conduct extensive experiments under various settings, demonstrating that FedNed can consistently outperform baselines and achieve state-of-the-art performance.

Abstract 2161: Recruitment feasibility of Hispanic women in breast cancer survivorship research using state cancer registry procedures

Abstract Objectives: Hispanic populations are underrepresented in epidemiologic studies. Although the Florida State Cancer Registry’s primary role is cancer surveillance and control, it can also aid in population-based epidemiologic studies of cancer survivors. This study aimed to evaluate the viability of using the Florida Cancer Data System’s (FCDS) patient recruitment methods to enlist Hispanic women with breast cancer for a cross-sectional survey. Methods: During August 2023 - November 2023, we recruited adult (age ≥ 20) women who were diagnosed with breast cancer at least six months ago before enrollment, self-identified themself as Hispanic, and currently living in Central Florida, using state-mandated recruitment procedure which includes two invitation letters sent via mail three weeks apart and calling to non-responders after three weeks, but up to 4 times. Potential interested participants were asked to complete a survey in English or Spanish via print or online packet, providing data on demographics, health, lifestyle, and quality of life. An Excel log was used to track the response and return dates. Results: As of October 25, 2023, we sent the invitation letters to a total of 667 participants deemed eligible per FCDS. Most of them did not respond to the letters or follow-up telephone calls, with 364 being unreachable due to incorrect contact information, which led to 303 accessible participants. The accessible sample included 22 Cubans, 73 Puerto Ricans, 65 South/Central Americans, 20 Dominicans, 39 Mexicans, and 83 Other Hispanics, with 48.7% speaking Spanish. Among the accessible participants, 113 refused to participate, 34 were not eligible, 13 have not decided yet, 118 agreed to participate but have not completed the survey yet, and 25 agreed to participate and completed the survey as of November 15. Common reasons for not qualifying included “not being Hispanic,” “dead,” “not living in Central Florida,” “not having cancer,” or “inability to read English or Spanish.” The most common reasons for refusal were “too sick,” “too old,” or “not interested.” The average response time to the first and second invitations was 20.1 and 19.6 days, respectively, and most participants responded to the second or third call within 8.3 days of the first call. Conclusions: The FCDS can be used to recruit Hispanics for epidemiology studies. However, the state-mandated patient recruitment procedure needs improvement. For example, the procedure should collect participants' email addresses in addition to their mail and telephone numbers, allow researchers to contact participants via email instead of written mail, and remove all dead individuals before sending the contact information to researchers. (Supported by Florida Breast Cancer Foundation Scientific Research Grant 2023 and UCF SEED grant). Citation Format: Eunkyung Lee, Brian Sukhu, Mia Griffin, Rhea Griffin, Nathalia Mercado-Almodovar, Nelisa Utrera, Keidy Bello, Meera Lakshmanan. Recruitment feasibility of Hispanic women in breast cancer survivorship research using state cancer registry procedures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2161.

Managing infectious diarrhea among young children in community pharmacies in Saudi Arabia and the implications for AMR.

Diarrhea remains a major global health issue for children under five, contributing substantially to morbidity and mortality. Community pharmacists play a pivotal role in the management of these children; however, their competence in managing childhood diarrhea in Saudi Arabia is under-researched. This is important to ensure optimal patient care. Simulated patients (SPs) presenting with three pediatric diarrhea scenarios were used to evaluate pharmacists' practice in terms of their counselling, history taking, over-the-counter (OTC) prescribing, medication instructions, diet/fluid advice, and/or information provision. Pharmacists' practice was categorized into adequate, less adequate, and poor. 182 community pharmacists, primarily male and non-Saudi, participated in the study, of which 60% were in chain pharmacies. Only 5% showed adequate practice in currently managing pediatric diarrhea. Of the 182 simulated patient visits, 62% received medication in all three scenarios and 20% were referred to physicians, with 16% of pharmacists failing to provide any form of intervention. The main medications recommended were kaolin (34%), pectin (34%) and metronidazole (11%). While most pharmacists (86%) asked about the patient's identity and age, 15% provided incorrect management information, 16% failed to provide guidance on the prescribed medicines, and 18% dispensed antimicrobials without a valid prescription. A high level of inadequate management of pediatric diarrhea in Saudi Arabia was observed. This highlights the need for extensive training to improve community pharmacists' practice in service delivery including providing counselling and advice on the appropriate management of childhood diarrhea. The latter is particularly important to reduce antimicrobial resistance.

O87: Stratified Evaluation of Large Language Model GPT-4’s Question-Answering In Surgery reveals AI Knowledge Gaps

Abstract Introduction Large Language Models (LLMs), such as GPT, are artificial intelligence models designed to analyse vast data and generate coherent outputs, changing the way healthcare professionals access knowledge. Critically, LLMs also present incorrect information confidently, a phenomenon known as hallucination, which is particularly dangerous in the safety-critical field of medicine. The validity of LLM responses to medical queries are being explored generally, however, responses to surgical questions remain poorly quantified. Variations between specialties are important to identify to support strategic LLM improvements. Methods We assessed accuracy of GPT-3 and GPT-4 in answering surgical multi-choice questions from the MedMCQA post-graduate question bank. We calculated the percentage accuracy of GPT-4 on all surgical questions (n=23025) and compared this to published GPT-4 performance across the whole MedMCQA dataset. We also analysed variations in performance by topic on a randomised sample of questions manually sorted by surgical specialty (n=1000). Results Accuracy rates for GPT-3 and GPT-4 were 53% and 64% respectively, demonstrating significant superiority of GPT-4, however GPT-4's surgical performance remained weaker than its overall MedMCQA performance. Notably, accuracy varied significantly by specialty, with strong performances in anatomy, vascular and paediatric surgery but below-average performances in orthopaedics, ENT and neurosurgery. Conclusion This study holds significant implications for the expanding use of LLMs in surgery, especially education. GPT has improved accuracy with sequential developments, however, its performance requires further scrutiny. We recommend ongoing attention on factors underpinning subject-variation in performance to aid strategic LLM innovation.

Quality issues of implied volatilities of index and stock options in the OptionMetrics IvyDB database

AbstractFor stock and index options in the United States, OptionMetrics records prices at 3:59 p.m., not 4:00 p.m. as assumed in previous literature. The resulting 1‐min time discrepancy with closing share prices creates artificial variability in implied volatility spreads and strongly affects market‐wide spreads. It leads to particularly large distortions at the onset of the COVID‐19 pandemic. For index options in Europe, OptionMetrics data show large deviations from put‐call parity even though the original option prices match the parity exactly. Finally, the implied volatilities of stock options in Europe show clusters of exceptional deviations due to incorrect dividend information.

Eğitim Kurumlarında İnsan Kaynakları Yönetimi Bağlamında Eğitim İhtiyaçlarının Önemi ve Amaçları

Educational needs are the starting point in the cycle of education and skill acquisition, and this cycle forms a continuous cycle that begins with identifying the learning needs of the target community. In addition, if the evaluation ignored the opinions of stakeholders or posed the wrong questions or contained incorrect information, the training to be provided will not coincide with the actual needs of the organization or its employees. It is possible for education to be effective by applying an effective educational needs analysis. The effectiveness of educational needs analysis, on the other hand, requires good planning, including its execution and clear returns. Learning objectives, designing education provides a high-quality tool to the organization in revealing the results of the education provided. Similarly, some researchers have stated that designing an education and development program involves a number of stages that can be grouped into five stages: educational needs analysis, educational objectives, design, implementation and evaluation. Long before a real education takes place, the education manager should determine down to the finest details in advance which methods and tools the education will be conducted in which educational institutions under the supervision of which experts. For this purpose, it is essential to determine the purpose of the educational needs analysis if the purpose of education is in the real sense. The purpose of the training needs analysis is to determine the performance requirements in the organization in order to direct those who are closely related to the areas where they are most needed, to fulfill the goals and objectives of the institution, to increase productivity, to provide quality products and services. Accordingly, educational needs are used as the basis for determining educational goals, choosing and designing educational programs, implementing programs, and evaluating the provided education as the first step in establishing an educational and development program. As a result of data collection and analysis, the need for education is determined, it becomes clear which education is required, and the type and scope of resources required to support an educational program can be examined. Key Words: Educational Institutions, Human Resources Management, the Importance and Purpose of Educational Needs

Quality, Accuracy, and Bias in ChatGPT-Based Summarization of Medical Abstracts.

Worldwide clinical knowledge is expanding rapidly, but physicians have sparse time to review scientific literature. Large language models (eg, Chat Generative Pretrained Transformer [ChatGPT]), might help summarize and prioritize research articles to review. However, large language models sometimes "hallucinate" incorrect information. We evaluated ChatGPT's ability to summarize 140 peer-reviewed abstracts from 14 journals. Physicians rated the quality, accuracy, and bias of the ChatGPT summaries. We also compared human ratings of relevance to various areas of medicine to ChatGPT relevance ratings. ChatGPT produced summaries that were 70% shorter (mean abstract length of 2,438 characters decreased to 739 characters). Summaries were nevertheless rated as high quality (median score 90, interquartile range [IQR] 87.0-92.5; scale 0-100), high accuracy (median 92.5, IQR 89.0-95.0), and low bias (median 0, IQR 0-7.5). Serious inaccuracies and hallucinations were uncommon. Classification of the relevance of entire journals to various fields of medicine closely mirrored physician classifications (nonlinear standard error of the regression [SER] 8.6 on a scale of 0-100). However, relevance classification for individual articles was much more modest (SER 22.3). Summaries generated by ChatGPT were 70% shorter than mean abstract length and were characterized by high quality, high accuracy, and low bias. Conversely, ChatGPT had modest ability to classify the relevance of articles to medical specialties. We suggest that ChatGPT can help family physicians accelerate review of the scientific literature and have developed software (pyJournalWatch) to support this application. Life-critical medical decisions should remain based on full, critical, and thoughtful evaluation of the full text of research articles in context with clinical guidelines.

Evaluating the Use of ChatGPT to Accurately Simplify Patient-centered Information about Breast Cancer Prevention and Screening.

Purpose To evaluate the use of ChatGPT as a tool to simplify answers to common questions about breast cancer prevention and screening. Materials and Methods In this retrospective, exploratory study, ChatGPT was requested to simplify responses to 25 questions about breast cancer to a sixth-grade reading level in March and August 2023. Simplified responses were evaluated for clinical appropriateness. All original and simplified responses were assessed for reading ease on the Flesch Reading Ease Index and for readability on five scales: Flesch-Kincaid Grade Level, Gunning Fog Index, Coleman-Liau Index, Automated Readability Index, and the Simple Measure of Gobbledygook (ie, SMOG) Index. Mean reading ease, readability, and word count were compared between original and simplified responses using paired t tests. McNemar test was used to compare the proportion of responses with adequate reading ease (score of 60 or greater) and readability (sixth-grade level). Results ChatGPT improved mean reading ease (original responses, 46 vs simplified responses, 70; P < .001) and readability (original, grade 13 vs simplified, grade 8.9; P < .001) and decreased word count (original, 193 vs simplified, 173; P < .001). Ninety-two percent (23 of 25) of simplified responses were considered clinically appropriate. All 25 (100%) simplified responses met criteria for adequate reading ease, compared with only two of 25 original responses (P < .001). Two of the 25 simplified responses (8%) met criteria for adequate readability. Conclusion ChatGPT simplified answers to common breast cancer screening and prevention questions by improving the readability by four grade levels, though the potential to produce incorrect information necessitates physician oversight when using this tool. Keywords: Mammography, Screening, Informatics, Breast, Education, Health Policy and Practice, Oncology, Technology Assessment Supplemental material is available for this article. © RSNA, 2023.

Eğitim Yönetiminde Kavramın Kuramsal Temeli ve Gelişim Süreci

People learn and use concepts, the words that refer to them, and the relationships between concepts at every age, starting from childhood. Dec. As they Decipher the relationships between concepts, they also generate new concepts and create new information, information networks and information clusters. Therefore, they make the concepts permanent by structuring them in their minds. Concepts are the basic means of thinking and can change at any time of life. Although a person has a limited amount of concept knowledge at first, he can change it through chance, experience and planned education. But while concepts learned by chance may be incomplete or incorrect, concepts can be learned correctly through education. Since it is more difficult to correct incorrect information than teaching something new, correcting misconceptions is also useful in terms of preventing information pollution and misunderstandings, understanding universal events and achieving goals. Therefore, it is of great importance to reduce misconceptions to the lowest level from childhood. However, according to the literature, although people have many misconceptions, they do not make efforts to correct them. Concepts were used in the production of scientific knowledge at the beginning of the twentieth century, while they served the function of representation in the mind. In this period, information production was being produced in line with certain theories, especially positivism, with the aim of an objective knowledge that is universally valid. However, from the 1980s onwards, postmodern and poststructuralists began to criticize objectivity. Accordingly, two opinions arose. According to the absolute subjectivists, the first view, the production of scientific knowledge was impossible, while according to the critical reality, the second view, hermonotic-based concepts required a harmony between Deceptions. People are expected to come to an agreement about concepts, otherwise the concepts would have no value. For this reason, concepts positively affect scientific activities in a field, as well as provide communication between the epistemic community. Dec. Therefore, social sciences is a field of production based on partnership, which includes reconciliation and sharing of concepts. Key Words: Educational Management, Concept, Theoretical Basis of the Concept

Description of a large specimen of Paralepis elongata (Brauer, 1906) from Angola, southeastern Atlantic Ocean (Aulopiformes: Paralepididae).

The elongate barracudina, Paralepis elongata (Brauer, 1906), received limited attention in the past, with only a few individuals documented, mostly in publications focusing on the feeding habits of large predatory pelagic fishes. Furthermore, the taxonomic history of the species is complicated, with some earlier sources providing incorrect information that influenced subsequent works. As a result, the accurate distribution range of P. elongata remains uncertain. An adult male specimen of P. elongata, representing the largest known individual, is reported from off Angola, southeastern Atlantic. A detailed description is provided, and the literature records of the species are reviewed and discussed.

Interpersonal Communication Patterns of Parents and Children in Preventing the Spread of Hoax Information

The development of information currently circulating requires sorting and selecting actions for all information obtained to prevent hoax information. One of the people who must play a role is parents by providing an understanding of the importance of media literacy. This research aims to find out 1) the methods used by parents in supervising their children's use of gadgets and social media, and 2) the role of parents in carrying out media literacy in children so that they can minimize the negative impact usage of gadgets and social media. The research was conducted using a qualitative approach with data collection techniques through interviews with three informants who already have children in their teens, and each owns a cellular phone. The analysis uses interpersonal communication theory by DeVito, He highlights the importance of effective communication in relationship development, emphasizing that it occurs within specific cultural, social, and environmental contexts. The results were processed by reducing the data, presenting the data, and verifying the data. The research results found that parents' actions towards children in monitoring the use of cellphones and social media are not an act of distrust towards children, but rather an effort to provide literacy to children about the impact of incorrect information.

A Truth or a Rumor: Effects of Addictive Substances ‎on Prevention of COVID-19; an Investigation of Homeless Drug Abusers in Southeastern Iran

Background: In a crisis, rumors often get started, and societies use them to cope with ambiguous situations. Southeastern Iran, with its high rate of homeless addicts per population, is a suitable area to examine the accuracy of the rumor about the positive effect of addictive substances on preventing COVID-19. Objectives: This cross-sectional study aimed to investigate the abundance of COVID-19 infection among homeless drug abusers in this area. Methods: For each participant, the health experts completed a questionnaire form. For a definite diagnosis of COVID-19, a combined oropharyngeal/nasal sampling method was used in the laboratory under standard polymerase chain reaction (RT-PCR). Data was analyzed with SPSS v. 19. The chi-square test was used to compare COVID-19 cases across substance types, abuse methods, and consumption frequencies. Results: In total, 295 homeless drug abusers participated, and positive COVID-19 was recorded for 21(7.1%). Most participants were waste pickers (60.5%), and 3.9% had COVID-19. The statistical analysis ‎showed no significant difference in the number of positive COVID-19 cases between narcotics, stimulants, and both combinations. Our study also did not prove the positive effect of drug abuse methods and the frequency of daily drug abuse on the incidence of this disease. Conclusions: During the COVID-19 pandemic, unreliable, incorrect, and incomplete information on its prevention spread in societies. It is recommended that health-related rumors be ‎investigated and officials publicly announce the results.‎

Hubungan Penggunaan ChatGPT Dengan Pemenuhan Kebutuhan Mahasiswa

The rise in the phenomenon of using Artificial Intelligence such as ChatGPT and the very high trust of the Indonesian people in the development of Artificial Intelligence, especially the Gen Z generation group with Higher Education status (students), has given rise to debate in terms of the impact of its uses as a technology that helps the learning process. On the positive side, ChatGPT can provide personalized and interactive learning, while on the negative side, ChatGPT can provide incorrect and inaccurate information. Meanwhile, penetration of its uses can no longer be avoided because it is easy to access, so the good and bad sides will continue to be felt, especially for its users. Moreover, until now there is still very little research on how this technology can help fulfill the needs of students within the research campus, especially the most important need for them as someone who is pursuing higher education, namely the need for satisfying learning. Therefore, this research aims to explain the relationship between the uses of ChatGPT and the fulfillment of learning needs for students at the Faculty of Communication Sciences, Padjadjaran University with the help of the Uses and Gratification theory approach. The research method usesd is a quantitative method with a type of correlational study design using the Spearman Rank correlation test The results of this study show that there is a positive and significant relationship between the uses of ChatGPT and fulfilling the learning needs of students at the Faculty of Communication Sciences, Padjadjaran University with a correlation coefficient of 0.834. So, it can be concluded that the higher the uses of ChatGPT, the higher the fulfillment of students' learning needs, which means that frequent uses of ChatGPT can indicate that it is closely related to satisfying learning needs.

Current aspects of vernal and atopic keratoconjunctivitis

Vernal keratoconjunctivitis (VKC) and atopic keratoconjunctivitis (AKC) are complex and rare diseases. Thus, their diagnosis and treatment are often achallenge. Discussion on the epidemiology, new pathogenetic concepts, interesting clinical findings, diagnostic possibilities and new treatment options and their side effects in severe ocular allergies. Analysis of the presentation of VKC in the internet. Evaluation of recent review articles, original publications, and case reports on the topics of VKC and AKC over the past 5years. Ocular allergies have significantly increased over the last decades. Recent concepts discussed in the pathogenesis of VKC and AKC are the role of the local and gut microbiome as well as the influence of neuroinflammation. Keratoconus is significantly more common in patients with VKC and AKC compared to the normal population. It is associated with faster progression and a more severe course of disease. Aconjunctival provocation test is only rarely necessary in the diagnosis of allergic conjunctivitis. Treatment of atopic dermatitis with dupilumab, an interleukin 4 receptor alpha (IL-4Ra) antagonist, can cause ocular side effects. Unfortunately, information available on the internet for patients and parents on the topic of VKC is sometimes dangerously incorrect. From the abovementioned new pathogenetic concepts, preventive and personalized treatment options could be developed in the future. Keratoconus in AKC/VKC must be recognized and treated early. Official guidelines are now available for astandardized conjunctival provocation test in the diagnosis of allergic conjunctivitis. The unwanted ocular side effects of dupilumab are often difficult to discriminate from the actual underlying AKC and respond well to anti-inflammatory treatment. Patients with VKC must be informed about the incorrect information on the internet regarding their disease.

A Comparative Study of Responses to Retina Questions from Either Experts, Expert-Edited Large Language Models, or Expert-Edited Large Language Models Alone

ObjectiveTo assess the quality, empathy, and safety of expert edited large language model (LLM), human expert created and LLM responses to common retina patient questions DesignRandomized, masked multicenter study ParticipantsTwenty-one common retina patient questions were randomly assigned among 13 retina specialists. Each expert created a response (Expert) and then edited a LLM (ChatGPT-4)-generated response to that question (Expert+AI), timing themselves for both tasks. Five LLMs (ChatGPT-3.5, ChatGPT-4, Claude 2, Bing, Bard) also generated responses to each question. The original question along with anonymized and randomized Expert+AI, Expert and LLM responses were evaluated by the other experts who did not write an expert response to the question. Evaluators judged quality and empathy (very poor, poor, acceptable, good, or very good) along with safety metrics (incorrect information, likelihood to cause harm, extent of harm, and missing content). Main OutcomeMean quality and empathy score, proportion of responses with incorrect information, likelihood to cause harm, extent of harm, and missing content for each response type ResultsThere were 4008 total grades collected (2608 for quality and empathy; 1400 for safety metrics), with significant differences in both quality and empathy (p<0.001, p<0.001) between LLM, Expert and Expert+AI groups. For quality, Expert+AI (3.86 +/- 0.85) performed the best overall while GPT-3.5 (3.75 +/- 0.79) was the top performing LLM. For empathy, GPT-3.5 (3.75 +/- 0.69) had the highest mean score followed by Expert+AI (3.73 +/- 0.63). By mean score, Expert placed fourth out of seven for quality and sixth out of seven for empathy. For both quality (p<0.001) and empathy (p<0.001), expert-edited LLM responses performed better than expert-created responses. There were time savings for an expert-edited LLM response vs. expert-created response (p=0.02). ChatGPT-4 performed similar to Expert for Inappropriate Content (p=0.35), Missing Content (p=0.001), Extent of Possible Harm (p=0.356), and Likelihood of Possible Harm (p=0.129). Conclusions and RelevanceIn this randomized, masked, multicenter study, LLM responses were comparable to experts in terms of quality, empathy and safety metrics, warranting further exploration of their potential benefits in clinical settings.

Rare disease in the age of artificial intelligence.

The text examines the impact of artificial intelligence (AI) in the context of rare diseases, exploring how patients turn to AI resources for health information, especially in situations where doctor-patient communication is limited. The article features the case of a doctor specializing in clinical psychology and psychotherapy, diagnosed with thymoma and Good's syndrome, who uses AI resources during his illness. The capabilities of five chatbots based on Large Language Models (LLMs), such as GPT-3.5, GPT-4, Bing Chat, Google Bard, and Anthropic Claude are explored. The AIs were queried on various aspects of the disease, from pre-diagnosis and diagnosis to therapeutic, psychological, and caregiver management issues. The responses were evaluated by five experts based on criteria such as: accuracy, relevance, coherence, clarity, practical utility, ethical considerations, empathy, and capacity to respond to questions and concerns. The results indicate consistency in the evaluators' assessments, with generally high scores across all dimensions. Particularly, systems like Bard and GPT-4 received high ratings in terms of information accuracy and the ability to respond to questions and concerns. Bing and Claude were appreciated for their empathy and tone. Overall, the AI systems' responses were considered appropriate, respectful of ethics and privacy, and useful in the clinical context. The article emphasizes the importance of understanding the reliability and precision of responses provided by AI systems in the clinical field. Although these systems offer high-quality responses, there is significant variability in their performance. Healthcare professionals must be aware of these differences and use such tools cautiously. AI can provide support in some aspects of care but cannot replace genuine human empathy and understanding. Integrating AI into clinical practice presents potential but also challenges, particularly the possibility of providing incorrect information. The AI systems demonstrate the ability to provide useful advice on clinical and psychological issues, but their use requires caution. It is crucial to distinguish the benefits of AI for patients from the challenges it presents for healthcare professionals. As AI technology continues to evolve, it is essential that its integration into the clinical field is accompanied by continuous research and evaluations, to ensure safe and effective use in the healthcare sector.

Knowledge and source type influence children’s skepticism of misinformation

ABSTRACT Three-year-olds often respond to lies as if they were true or with no clear rationale. Individual differences influence children’s processing of misinformation. Here, we explore how two contextual cues (children’s conflicting first-hand knowledge and different information sources) affect their ability to correctly interpret and respond to misinformation. 133 three-year-olds from the northeastern United States searched for a hidden object after it had traveled down one of two criss-crossing tubes. Children played this game by themselves (baseline), with a deceptive human (intentionally incorrect about the location of the object), a broken human (unintentionally incorrect), or a broken machine (incapable of intentions and incorrect). Half of the children could see the object as it traveled down the tubes (knowledgeable group), and the other half could not (no-knowledge group). With this first-hand knowledge, children were more likely to search correctly when playing by themselves. Information source further impacted children’s responses. Responding to a deceptive human was the most challenging: knowledgeable children searched randomly; unknowledgeable children inappropriately deferred to the deceptive experimenter. But when knowledgeable children played with either the broken human or the broken machine, they had as many correct searches as when they played alone. Context may have its limits when it comes to influencing children’s responses to misinformation. Specifically, although first-hand knowledge appears to help 3-year-olds correctly respond to some forms of incorrect information, it may not be enough to help them overcome misinformation from explicitly deceptive sources.

Pelanggaran Prinsip Kerja Sama dalam Drama “Sweet Home” (스위트홈)

This article examines the forms of violation of Grice's maxim of cooperation principles carried out by the characters in the drama Sweet Home (SH) and their relationship with achieving communication goals. This research uses a descriptive-qualitative method referring to Grice's theory of Cooperation Principles (CP) (1975). Research data was obtained using the method of listening without being involved and taking notes. Based on the research results, four forms of single PKS violations were found in SH: the maxims of quantity, quality, relevance, and manner. Apart from these violations, double maxim violations were also found, namely violations of the maxim of relevance and the maxim of manner. The causes of CP violations occur due to the following factors: (1) in the maxim of quantity, the speaker provides excessive information; (2) in the maxim of quality, the speaker conveys incorrect information and uses similes and figures of speech; (3) in the maxim of relevance, the speaker gives information that is implicit and unrelated to the topic of conversation; (4) in the maxim of manner, the speaker sends an ambiguous message, not straightforward and long-winded. This research shows that most conversations that violate the maxim of relevance, the maxim of quality, and the maxim of manner fail to achieve communication goals because the recipient of the message does not understand the message. on the contrary, failure of communication objectives does not occur in a maxim of quantity but only causes ineffectiveness in delivering the message.