Traditional Search Engines Research Articles

S672 Accuracy and Helpfulness of Chat GPT Compared to Traditional Search Engines for Gastroesophageal Reflux Disease (GERD) Queries: A New Era of Information Retrieval

S672 Accuracy and Helpfulness of Chat GPT Compared to Traditional Search Engines for Gastroesophageal Reflux Disease (GERD) Queries: A New Era of Information Retrieval

Navigating Technological Shifts: An Examination of User Inertia and Technology Prestige in Large-Language-Model AI Chatbot Transition

As large-language-model (LLM) AI chatbots create new pathways for problem-solving, they pose a challenge to the dominance of traditional search engines and knowledge sharing platforms. Utilizing theories of Status Quo Bias and Symbolic Consumption, this study investigates how user inertia and the perceived prestige of LLM AI chatbots influence the transition between platforms. A national survey comprising participants who have used LLM AI chatbots, knowledge sharing forums, and search engines (N = 800) within China was conducted. The findings obtained from structural equation modeling reveal that user inertia, rooted in habits and sunk costs, inhibits platform transition; however, the prestige of LLM AI chatbots can promote adoption and mitigate inertia; and entrenched media habits could, surprisingly, facilitate openness to new technologies. Implications on platform migration behavior amid technological advancements are discussed.

Online Patient Education in Obstructive Sleep Apnea: ChatGPT versus Google Search.

The widespread implementation of artificial intelligence technologies provides an appealing alternative to traditional search engines for online patient healthcare education. This study assessed ChatGPT-3.5's capabilities as a source of obstructive sleep apnea (OSA) information, using Google Search as a comparison. Ten frequently searched questions related to OSA were entered into Google Search and ChatGPT-3.5. The responses were assessed by two independent researchers using the Global Quality Score (GQS), Patient Education Materials Assessment Tool (PEMAT), DISCERN instrument, CLEAR tool, and readability scores (Flesch Reading Ease and Flesch-Kincaid Grade Level). ChatGPT-3.5 significantly outperformed Google Search in terms of GQS (5.00 vs. 2.50, p < 0.0001), DISCERN reliability (35.00 vs. 29.50, p = 0.001), and quality (11.50 vs. 7.00, p = 0.02). The CLEAR tool scores indicated that ChatGPT-3.5 provided excellent content (25.00 vs. 15.50, p < 0.001). PEMAT scores showed higher understandability (60-91% vs. 44-80%) and actionability for ChatGPT-3.5 (0-40% vs. 0%). Readability analysis revealed that Google Search responses were easier to read (FRE: 56.05 vs. 22.00; FKGL: 9.00 vs. 14.00, p < 0.0001). ChatGPT-3.5 delivers higher quality and more comprehensive OSA information compared to Google Search, although its responses are less readable. This suggests that while ChatGPT-3.5 can be a valuable tool for patient education, efforts to improve readability are necessary to ensure accessibility and utility for all patients. Healthcare providers should be aware of the strengths and weaknesses of various healthcare information resources and emphasize the importance of critically evaluating online health information, advising patients on its reliability and relevance.

Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry

This study explores the cognitive load and learning outcomes associated with using large language models (LLMs) versus traditional search engines for information gathering during learning. A total of 91 university students were randomly assigned to either use ChatGPT3.5 or Google to research the socio-scientific issue of nanoparticles in sunscreen to derive valid recommendations and justifications. The study aimed to investigate potential differences in cognitive load, as well as the quality and homogeneity of the students' recommendations and justifications. Results indicated that students using LLMs experienced significantly lower cognitive load. However, despite this reduction, these students demonstrated lower-quality reasoning and argumentation in their final recommendations compared to those who used traditional search engines. Further, the homogeneity of the recommendations and justifications did not differ significantly between the two groups, suggesting that LLMs did not restrict the diversity of students’ perspectives. These findings highlight the nuanced implications of digital tools on learning, suggesting that while LLMs can decrease the cognitive burden associated with information gathering during a learning task, they may not promote deeper engagement with content necessary for high-quality learning per se.

Competition concerns with foundation models: a new feast for big tech?

ABSTRACT The paper explores how Generative AI intersects with Competition Law, focusing on Foundation Models (FMs) and Large Language Models (LLMs). It examines industry dynamics and identifies key competition issues like entry barriers, tying, leveraging, and acquisitions. It highlights the supply chain's importance and looks at how FMs are integrated into search software, chatbots, and productivity tools, particularly noting entry barriers such as computing power and data collection. It suggests that FMs might require new approaches to market delineation, possibly creating a separate relevant market for data. The paper also discusses various cases pertaining to tying and leveraging and highlights the difficulty in proving tying due to the blurred lines between traditional search engines and AI chatbots. It illustrates how competition assessments for acquisitions may require changes due to data being a highly flexible commodity for the industry. The paper concludes by calling for increased scrutiny and regulation for the industry.

Does generative artificial intelligence pose a risk to performance validity test security?

Objective We examined the performance validity test (PVT) security risk presented by artificial intelligence (AI) chatbots asking questions about neuropsychological evaluation and PVTs on two popular generative AI sites. Method In 2023 and 2024, multiple questions were posed to ChatGPT-3 and Bard (now Gemini). One set started generally and refined follow-up questions based on AI responses. A second set asked how to feign, fake, or cheat. Responses were aggregated and independently rated for inaccuracy and threat. Responses not identified as inaccurate were assigned a four-level threat rating (no, mild, moderate, or high threat). Combined inaccuracy and threat ratings were examined cross-sectionally and longitudinally. Results Combined inaccuracy rating percentages were 35 to 42% in 2023 and 16 to 28% in 2024. Combined moderate/high threat ratings were observed in 24 to 41% of responses in 2023 and in 17 to 31% of responses in 2024. More ChatGPT-3 responses were rated moderate or high threat compared to Bard/Gemini responses. Over time, ChatGPT-3 responses became more accurate with a similar threat level, but Bard/Gemini responses did not change in accuracy or threat. Responses to how to feign queries demonstrated ethical opposition to feigning. Responses to similar queries in 2024 showed even stronger ethical opposition. Conclusions AI chatbots are a threat to PVT test security. A proportion of responses were rated as moderate or high threat. Although ethical opposition to feigning guidance increased over time, the natural language interface and the volume of AI chatbot responses represent a potentially greater threat than traditional search engines.

Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification

Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities, and future perspectives of this approach and its impact on mass spectrometry-based proteomics.

Using Google web search to analyze and evaluate the application of ChatGPT in femoroacetabular impingement syndrome.

Chat Generative Pre-trained Transformer (ChatGPT) is a new machine learning tool that allows patients to access health information online, specifically compared to Google, the most commonly used search engine in the United States. Patients can use ChatGPT to better understand medical issues. This study compared the two search engines based on: (i) frequently asked questions (FAQs) about Femoroacetabular Impingement Syndrome (FAI), (ii) the corresponding answers to these FAQs, and (iii) the most FAQs yielding a numerical response. To assess the suitability of ChatGPT as an online health information resource for patients by replicating their internet searches. Cross-sectional study. The same keywords were used to search the 10 most common questions about FAI on both Google and ChatGPT. The responses from both search engines were recorded and analyzed. Of the 20 questions, 8 (40%) were similar. Among the 10 questions searched on Google, 7 were provided by a medical practice. For numerical questions, there was a notable difference in answers between Google and ChatGPT for 3 out of the top 5 most common questions (60%). Expert evaluation indicated that 67.5% of experts were satisfied or highly satisfied with the accuracy of ChatGPT's descriptions of both conservative and surgical treatment options for FAI. Additionally, 62.5% of experts were satisfied or highly satisfied with the safety of the information provided. Regarding the etiology of FAI, including cam and pincer impingements, 52.5% of experts expressed satisfaction or high satisfaction with ChatGPT's explanations. Overall, 62.5% of experts affirmed that ChatGPT could serve effectively as a reliable medical resource for initial information retrieval. This study confirms that ChatGPT, despite being a new tool, shows significant potential as a supplementary resource for health information on FAI. Expert evaluations commend its capacity to provide accurate and comprehensive responses, valued by medical professionals for relevance and safety. Nonetheless, continuous improvements in its medical content's depth and precision are recommended for ongoing reliability. While ChatGPT offers a promising alternative to traditional search engines, meticulous validation is imperative before it can be fully embraced as a trusted medical resource.

Suitable and Style-Consistent Multi-Texture Recommendation for Cartoon Illustrations

Texture plays an important role in cartoon illustrations to display object materials and enrich visual experiences. Unfortunately, manually designing and drawing an appropriate texture is not easy even for proficient artists, let alone novice or amateur people. While there exist tons of textures on the Internet, it is not easy to pick an appropriate one using traditional text-based search engines. Although several texture pickers have been proposed, they still require the users to browse the textures by themselves, which is still labor-intensive and time-consuming. In this article, an automatic texture recommendation system is proposed for recommending multiple textures to replace a set of user-specified regions in a cartoon illustration with visually pleasant look. Two measurements, the suitability measurement and the style-consistency measurement, are proposed to make sure that the recommended textures are suitable for cartoon illustration and at the same time mutually consistent in style. The suitability is measured based on the synthesizability, cartoonity, and region fitness of textures. The style-consistency is predicted using a learning-based solution since it is subjective to judge whether two textures are consistent in style. An optimization problem is formulated and solved via the genetic algorithm. Our method is validated on various cartoon illustrations, and convincing results are obtained.

Public Opinions on ChatGPT : An Analysis of Reddit Discussions by Using Sentiment Analysis, Topic Modeling, and SWOT Analysis

ABSTRACT The sudden arrival of AI (Artificial Intelligence) into people's daily lives all around the world was marked by the introduction of ChatGPT, which was officially released on November 30, 2022. This AI invasion in our lives drew the attention of not only tech enthusiasts but also scholars from diverse fields, as its capacity extends across various fields. Consequently, numerous articles and journals have been discussing ChatGPT, making it a headline for several topics. However, it does not reflect most public opinion about the product. Therefore, this paper investigated the public's opinions on ChatGPT through topic modelling, Vader-based sentiment analysis and SWOT analysis. To gather data for this study, 202905 comments from the Reddit platform were collected between December 2022 and December 2023. The findings reveal that the Reddit community engaged in discussions related to ChatGPT, covering a range of topics including comparisons with traditional search engines, the impacts on software development, job market, and education industry, exploring ChatGPT's responses on entertainment and politics, the responses from Dan, the alter ego of ChatGPT, the ethical usage of user data as well as queries related to the AI-generated images. The sentiment analysis indicates that most people hold positive views towards this innovative technology across these several aspects. However, concerns also arise regarding the potential negative impacts associated with this product. The SWOT analysis of these results highlights both the strengths and pain points, market opportunities and threats associated with ChatGPT. This analysis also serves as a foundation for providing recommendations aimed at the product development and policy implementation in this paper.

Creating Search Engine Using Machine Learning Methods

Abstract: The vast and ever-expanding amount of information available in the WWW has led to the widespread usage of search engines for data retrieval. It can be challenging to locate information that is actually relevant and helpful, even while ordinary search engines offer users an intuitive interface for entering queries and retrieving web page links as results. To rectify that problem, which paper presents a novel search engine that work ML techniques. The target is to come up users with most relevant web sites when they query the engine. The suggested search engine improves the relevancy and accuracy of search results by utilizing machine learning algorithms. This system seeks to grasp user intent, adjust to individual preferences, and deliver contextually relevant information by going beyond the bounds of traditional search engines. By using machine learning models, the search engine may learn dynamically and enhance the standard of its results over time by continuously refining its understanding. More sophisticated and adaptable search engines are being developed as an outcome of these combination of state-of-the-art machine learning libraries, tools for processing natural language, and effective indexing systems. The target of the research is too advance information retrieval systems by providing a more advanced and user-focused method of tackling the difficulties presented by the WWW's immense scope

How Does ChatGPT Use Source Information Compared With Google? A Text Network Analysis of Online Health Information.

How Does ChatGPT Use Source Information Compared With Google? A Text Network Analysis of Online Health Information.

ChatGPT effects on cognitive skills of undergraduate students: Receiving instant responses from AI-based conversational large language models (LLMs)

This study investigated the impact of using ChatGPT, a state-of-the-art generative AI-based model, on the critical, creative, and reflective thinking skills of university students in Ghana. The study utilized a mixed-methods research approach, incorporating quantitative and qualitative data collection instruments, and an experimental procedure with a pretest-posttest control group. The study ultimately enlisted a sample of 125 students randomly allocated to either the experiment group (60 students) or the control group (65 students). The research was conducted in the context of a Research Methodology course, which had adopted the flipped classroom approach. The students in the experiment group engaged with ChatGPT for in-class tasks, while those in the control group used traditional databases and search engines for similar tasks. Data were collected using the Critical Thinking Scale, Creative Thinking Scale, Reflective Thinking Scale, and a student interview guide (semi-structured). The study's findings illustrated that incorporating ChatGPT influenced the students' critical, reflective, and creative thinking skills and their dimensions discernibly. As a result, the study provides suggestions for academics, instructional designers, and researchers working in educational technology.

ADMIRATION, SKEPTICISM, INDECISIVENESS? ATTITUDES TOWARD AI IN CHINESE MIDDLE-AGED AND OLDER ADULTS

Abstract In recent years, AI is increasingly visible in daily lives from chatbots in banking apps and SIRI, and generative AI such as ChatGPT, to more controversial usage concerning facial recognition, online surveillance, and even self-driving cars. Attitudes toward AI may predict acceptance of AI-facilitated applications and the Web 3.0 at-large. In June 2023, we conducted a survey with 750 smartphone users aged 45 years or older (Max = 78) in Hong Kong as a part of a wider effort to elucidate the digital divide post-pandemic. Using principal component analysis with varimax rotation, we found three components from the General Attitude to Artificial Intelligence Scale - Negative, Positive, and Powerful (compared to humans). Subsequently, with latent profile analysis we revealed three profiles: (i) Admiration (18.4%; high on positive and powerful but low on negative); (ii) Skepticism (12.3%; high on negative but low on positive and powerful), and (iii) Indecisiveness (69.3%; moderate on all three components). Admiration was more likely to be male, with higher income, better self-rated health, and greater mobile device proficiency, optimism, innovativeness, but also insecurity with technology, compared to Indecisiveness, and then Skepticism. Corroborating these quantitative findings, AI, or generative AI in specific, were only sparsely featured in a concurrent focus group study on smartphone usage with a similar cohort of smartphone users. Those who mentioned were appreciative of their superior power to provide information beyond traditional search engines; yet apprehension toward associated fraud and deception was also highlighted.

EXPLORING THE EFFICACY OF ARTIFICIAL INTELLIGENCE IN DELIVERING HOSPICE INFORMATION

Abstract Background Artificial Intelligence Chatbots (AIC) are swiftly gaining popularity due to their language model capability to address user queries by analyzing extensive amounts of available data and generating human-like responses. Objective: To analyze the accuracy of AIC in responding to inquiries related to hospice services and benefits. Additionally, the goal is to assess the user-friendliness of AIC in comparison to traditional web search engines. Methods AICs, namely ChatGPT and Bard, were subjected to testing through a series of questions and scenarios pertinent to hospice services (such as its definition, eligibility criteria, types of provided services, and specific care scenarios). Recorded responses from the AICs were subsequently evaluated for accuracy and quality by experts in geriatric palliative care. Results Both ChatGPT and Bard provided mostly accurate responses to fundamental questions and uncomplicated scenarios regarding hospice. These encompassed explanations about prognosis, interdisciplinary teams, and basic services offered. While ChatGPT did not furnish articles or source references, it did offer occasional medical advice. On the other hand, Bard issued medical information warnings and occasionally included visual aids like pictures, diagrams, and website links. When faced with the same set of questions, conventional web search engines often supplied links to webpages that did not invariably contain specific answers to the queries. Conclusions The AICs can deliver predominantly accurate, generic information about hospice in a conversational and comprehensible manner. Future research could delve into how AICs would respond to more personalized and specific inquiries that could be posed by patients or caregivers.

Revolutionizing digital marketing: unveiling the impact of influencer marketing, AI-driven customer support, and voice search optimization on engagement and efficiency on the example of the semiconductor manufacturing industry

In the rapidly evolving landscape of semiconductor manufacturing, this research paper explores the application of pivotal digital marketing trends and strategic innovations to reshape contemporary business strategies within the industry. Recognizing the significance of staying at the forefront of these transformations, our study aims to provide a nuanced understanding of three prominent digital marketing innovations: Influencer Marketing, Artificial Intelligence (AI)-driven Customer Support, and Voice Search Optimization. Through a rigorous methodology integrating literature review and case study analysis specific to semiconductor manufacturing in India, we unveil the successful implementation and tangible impact of these innovations on user engagement, brand perception, and overall marketing efficacy within the semiconductor sector of India. The research was done in 2022-2023 over the semiconductor companies working in India (HCL Technologies, Bharat Electronics Limited, ABB India Limited, Vedanta, CG Power and Industrial Solutions Limited, and Havells India Limited). Comparative analyses against traditional methods reveal significant improvements tailored to this industry: Influencer Marketing campaigns demonstrated a 25% increase in reach and engagement compared to conventional advertising. The integration of AI-driven Customer Support resulted in a 30% reduction in response time, enhancing customer satisfaction scores by 15%. Furthermore, Voice Search Optimization strategies yielded a 20% improvement in search accuracy, outperforming traditional text-based Search Engine Optimization (SEO) approaches within the semiconductor manufacturing context. Delving into challenges and opportunities associated with each innovation within the semiconductor industry, we provide evidence-based recommendations for businesses seeking to navigate the dynamic digital marketing landscape in this specialized domain. As semiconductor manufacturing continues to redefine its business ecosystem, embracing these innovations emerges as a cornerstone for sustained success in the digital era within this unique sector.

Factors Influencing ChatGpt Adoption for Product Research and Information Retrieval

ABSTRACT In recent months, Generative AI-based systems have become increasingly prevalent for public use. Traditionally, people use Google and other search platforms to research a product before purchasing. However, with the integration of ChatGPT into Bing and Google’s announcement of BARD, these models have the potential to impact how customers search for and discover products significantly. This article aims to understand whether people will shift from traditional to Generative AI-based search engines. The research examines the factors influencing purchase decisions and the likelihood of individuals using ChatGPT instead of traditional search engines and other sources. The results indicate that gender, product knowledge, and preference for online or retail store purchases can help predict their inclination to switch to ChatGPT. The paper also discusses people’s trust in Generative AI and the influence of demographic features on their attitude toward these machine learning systems.

The Future of Patient Education: AI-Driven Guide for Type 2 Diabetes.

Introduction and aim The surging incidence of type 2 diabetes has become a growing concern for the healthcare sector. This chronic ailment, characterized by its complex blend of genetic and lifestyle determinants, has witnessed a notable increase in recent times, exerting substantial pressure on healthcare resources. As more individuals turn to online platforms for health guidance and embrace the utilization of Chat Generative Pre-trained Transformer (ChatGPT; San Francisco, CA: OpenAI), a text-generating AI (TGAI), to get insights into their well-being, evaluating its effectiveness and reliability becomes crucial.This research primarily aimed to evaluate the correctness of TGAI responses to type 2 diabetes (T2DM) inquiries via ChatGPT. Furthermore, this study aimed to examine the consistency of TGAI in addressing common queries on T2DM complications for patient education. Material and methods Questions on T2DM were formulated by experienced physicians and screened by research personnel before querying ChatGPT. Each question was posed thrice, and the collected answers were summarized. Responses were then sorted into three distinct categories as follows: (a) appropriate, (b) inappropriate, and (c) unreliable by two seasoned physicians. In instances of differing opinions, a third physician was consulted to achieve consensus. Results From the initial set of 110 T2DM questions, 40 were dismissed by experts for relevance, resulting in a final count of 70. An overwhelming 98.5% of the AI's answers were judged as appropriate, thus underscoring its reliability over traditional online search engines. Nonetheless, a 1.5% rate of inappropriate responses underlines the importance of ongoing AI improvements and strict adherence to medical protocols. Conclusion TGAI provides medical information of high quality and reliability. This study underscores TGAI's impressive effectiveness in delivering reliable information about T2DM, with 98.5% of responses aligning with the standard of care. These results hold promise for integrating AI platforms as supplementary tools to enhance patient education and outcomes.

Collaborative Search Model for Lost-Link Borrowers Information Based on Multi-Agent Q-Learning

To reduce the economic losses caused by debt evasion amongst lost-link borrowers (LBs) and improve the efficiency of finding information on LBs, this paper focuses on the cross-platform information collaborative search optimization problem for LBs. Given the limitations of platform/system heterogeneity, data type diversity, and the complexity of collaborative control in cross-platform information search for LBs, a collaborative search model for LBs’ information based on multi-agent technology is proposed. Additionally, a multi-agent Q-learning algorithm for the collaborative scheduling of multi-search subtasks is designed. We use the Q-learning algorithm based on function approximation to update the description model of the LBs. The multi-agent collaborative search problem is transformed into a reinforcement learning problem by defining search states, search actions, and reward functions. The results indicate that: (i) this model greatly improves the comprehensiveness and accuracy of the search for key information of LBs compared with traditional search engines; (ii) during searching for the information of LBs, the agent is more inclined to search on platforms and data types with larger environmental rewards, and the multi-agent Q-learning algorithm has a stronger ability to acquire information value than the transition probability matrix algorithm and the probability statistical algorithm for the same number of searches; (iii) the optimal search times of the multi-agent Q-learning algorithm are between 14 and 100. Users can flexibly set the number of searches within this range. It is significant for improving the efficiency of finding key information related to LBs.

DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research.

The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research. Traditional keyword-based search engines often fall short in assisting users who may not be familiar with specific terminologies. To address this, we present a knowledge graph based paper search engine for biomedical research to enhance the user experience in discovering relevant queries and articles. The system, dubbed DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS) tagging to extract terminologies and relationships from article abstracts to create a KG. To reduce information overload, DiscoverPath presents users with a focused subgraph containing the queried entity and its neighboring nodes and incorporates a query recommendation system enabling users to iteratively refine their queries. The system is equipped with an accessible Graphical User Interface that provides an intuitive visualization of the KG, query recommendations, and detailed article information, enabling efficient article retrieval, thus fostering interdisciplinary knowledge exploration. DiscoverPath is open-sourced at https://github.com/ynchuang/DiscoverPath with a demo video at Youtube.