Artificial Intelligence Tools Research Articles

The anthropomorphic pursuit of AI-generated journalistic texts: limits to expressing subjectivity

Many newsrooms around the world are increasingly turning to artificial intelligence (AI) algorithms to generate journalistic content. Often, these machine-generated texts are distributed without being clearly identified as synthetic or hybrid. Since the launch of ChatGPT in late 2022, the tool’s extraordinary ability to mimic human language has been widely celebrated. Given that subjectivity is an integral part of human language, this study examines how different texts generated using AI tools are imbued with subjective features in order to anthropomorphize their linguistic content. Our aim is to gain insight into the ways in which these texts express subjectivity in order to appear anthropomorphic, as well as the limits of this expression and its implications for communication. To this end, a corpus of AI-generated journalistic texts published in various media, as well as texts created using AI tools such as ChatGPT and Gemini, is analysed to assess these tools’ capabilities. Ten criteria are used to characterize the expression of subjectivity in journalistic discourse on the surface of texts and in terms of situational appropriateness. The results show that AI tools can incorporate subjective markers on the text surface, but have important limitations when it comes to situational appropriateness, making it difficult to imitate certain features of journalistic writing. The paper also discusses the implications of asymmetrical audience interaction with machines that simulate human characteristics, and the varying degrees of opacity and transparency with which AI is used in newsrooms.

The Current Progress of Artificial Intelligence in Approach to Thyroid Nodules: A Narrative Review

Background: Artificial intelligence (AI) can play a significant role in the future of thyroidology. Thyroid nodules are common conditions that may benefit from AI through more accurate and efficient diagnosis, risk stratification, and medical or surgical management. Objective: This paper aims to review the latest developments in AI applications for diagnosing and managing thyroid nodules and cancers. Methods: English full-text articles published in the PubMed and Google Scholar databases from January 2014 to March 2024 were collected and reviewed to provide a comprehensive understanding of the topic. A total of 45 studies were selected based on relevance, robust methodology, statistical significance, and broader topic coverage. Results: Artificial intelligence has emerged as a powerful tool for managing thyroid nodules. First, several studies have demonstrated how AI-powered ultrasound interpretation enhances the diagnosis and classification of nodules while reducing the need for invasive fine-needle aspiration (FNA) biopsies. Second, AI significantly improves the cytopathological differentiation between benign and malignant thyroid nodules by minimizing reliance on pathologists' expertise and implementing standardized diagnostic criteria. When cytopathology is inconclusive, AI also aids in identifying molecular markers from omics data, distinguishing between normal and cancerous cells. Moreover, AI tools have been developed for prognosis assessment, predicting distant metastasis, recurrence, and surveillance by integrating medical imaging features with molecular and clinical factors. Additionally, some AI tools are designed for intraoperative evaluation, improving surgical techniques and reducing complications during thyroidectomy. In non-surgical treatments, several models have been developed to optimize therapeutic doses of radioactive iodine (RAI) and predict the outcomes of new drug formulations. Conclusions: Artificial intelligence has the potential to assist physicians in accurate thyroid nodule diagnosis, classification, decision-making, optimizing treatment strategies, and improving patient outcomes. However, there are still limitations to this technology. Artificial intelligence-driven tools require further advancements before they can be fully integrated into clinical practice and replace specialists.

DIRBTINIS INTELEKTAS IR UGDYMAS: KELETAS APMĄSTYMŲ

Artificial Intelligence (AI) has been a subject of growing interest and concern recently. Although it is not a particularly new technology in a general sense, the free availability of certain AI tools has created a certain momentum. AI is becoming an increasingly popular topic every year and is generating a lot of controversy, but even with the increased attention, most people are not aware of what AI actually is and that it has long been a crucial part of our daily lives. It is clear that the ever-evolving tools of the AI are nowadays not bypassing the field of education. The role of the teacher is an important aspect in the context of the development and penetration of AI in education. AI may have an impact on the role of teachers, but this will be a complementary form of change rather than a complete replacement of teachers. While various AI tools may help teachers to be more effective in their tasks, the aspects of human interaction, motivation and empathy that are important for the teacher are likely to remain unchanged. It is clear that the spread/penetration of AI in education will lead to an overall paradigm shift, such as a massive shift towards project-based learning/study and a fundamental change in the role of the teacher/lecturer. The field of education is conservative in nature and based on tradition. Therefore, innovations are usually not the first to come, but are tried and tested in other areas. Artificial intelligence is thus becoming a key player in the field of education, opening up new perspectives and challenges. AI is not only a tool, but also a new aspect of education. We are undoubtedly on the verge of unique opportunities, but at the same time we are facing complex challenges both known and unknown. It is becoming important to develop effective strategies for introducing AI into education, while preserving the values and overarching goals of education.

Assessment of the Diagnostic Performance of a Commercially Available Artificial Intelligence Algorithm for Risk Stratification of Thyroid Nodules on Ultrasound.

Background: Thyroid nodules are challenging to accurately characterize on ultrasound (US), though the emergence of risk stratification systems and more recently artificial intelligence (AI) algorithms has improved nodule classification. The purpose of this study was to evaluate the performance of a recent Food and Drug Administration (FDA)-cleared AI tool for detection of malignancy in thyroid nodules on US. Methods: One year of consecutive thyroid US with ≥1 nodule from Duke University Hospital and its affiliate community hospital (649 nodules from 347 patients) were retrospectively evaluated. Included nodules had ground truth diagnoses by surgical pathology, fine needle aspiration (FNA), or three-year follow-up US showing stability. An FDA-cleared AI tool (Koios DS Thyroid) analyzed each nodule to generate (i) American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) descriptors, scores, and follow-up recommendations and (ii) an AI-adapter score to further adjust risk assessments and recommendations. Four groups were then compared: (i) Koios with AI-adapter, (ii) Koios without AI-adapter, (iii) clinical radiology report, and (iv) radiology report combined with AI-adapter. Performance of the final recommendations (FNA or no FNA) was determined based on ground truth, and comparison between the four groups was made using sensitivity, specificity, and receiver-operating-curve analysis. Results: Of 649 nodules, 32 were malignant and 617 were benign. Performance of Koios with AI-adapter enabled was similar to radiologists (area under the curve [AUC] 0.70 for both, [CI 0.60-0.81] and [0.60-0.79], respectively). Koios with AI-adapter had improved specificity compared to radiologists (0.63 [CI: 0.59-0.67] versus 0.43 [CI: 0.38-0.48]) but decreased sensitivity (0.69 [CI: 0.50-0.83) versus 0.81 [CI: 0.61, 0.92]). Highest performance was seen when the radiology interpretation was combined with the AI-adapter (AUC 0.76, [CI: 0.67-0.85]). Combined with the AI-adapter, radiologist specificity improved from 0.43 ([CI: 0.38-0.48]) to 0.53 ([CI: 0.49-0.58]) (McNemar's test p < 0.001), resulting in 17% fewer FNA recommendations, with unchanged sensitivity (0.81, p = 1). Conclusion: Koios DS demonstrated standalone performance similar to radiologists, though with lower sensitivity and higher specificity. Performance was best when radiologist interpretations were combined with the AI-adapter component, with improved specificity and reduced unnecessary FNA recommendations.

Multimodal optimization of concrete mix design for sustainable load bearing wall panels: Mean-mix − Artificial Intelligence − experimentation fusion

This work used abundant database to intelligently select a mix-proportion of conventional OPC and fly ash (FA) based concretes. First, data was sorted and bifurcated according to the grades of concrete, and a mean mix-design (MMD) approach was proposed for selecting constituents. Afterward, both data types were trained using an artificial intelligence (AI) tool, and strength was predicted for trained sets and testing sets of data. It was further compared with the MMD approach for considering a 30 MPa strength. Moreover, experiments were designed for 30 MPa targeted strength by ACI and MMD methods. It was observed that MMD, AI, and experimentation approaches have close correspondence for selected targeted strength. However, ACI-based mix-design overestimated the strength, resulting in higher costs and CO2 emissions than their counterparts. It has also been observed that FA-based concrete has a similar cost to lower-grade conventional concrete but has lesser CO2 emissions. Furthermore, a multi-objective optimization model was proposed for intelligent mix design by integrating strength, cost, and CO2 emissions. It was revealed that the proposed MMD proportions of constituents are among the top-ranked intelligent mixes, thus verifying the proposed approach. Finally, the wall panel was developed of size 600 × 300 × 125 mm using conventional concrete and FA-based concrete, and it fulfills the performance requirement of masonry defined by standards. The cost of masonry with wall panels is 35 % lower than conventional brick masonry, and CO2 emission was 68 % lower, respectively.

The unreliability of crackles: insights from a breath sound study using physicians and artificial intelligence

Background and introductionIn comparison to other physical assessment methods, the inconsistency in respiratory evaluations continues to pose a major issue and challenge.ObjectivesThis study aims to evaluate the difference in the identification ability of different breath sound.Methods/descriptionIn this prospective study, breath sounds from the Formosa Archive of Breath Sound were labeled by five physicians. Six artificial intelligence (AI) breath sound interpretation models were developed based on all labeled data and the labels from the five physicians, respectively. After labeling by AIs and physicians, labels with discrepancy were considered doubtful and relabeled by two additional physicians. The final labels were determined by a majority vote among the physicians. The capability of breath sound identification for humans and AI was evaluated using sensitivity, specificity and the area under the receiver-operating characteristic curve (AUROC).Results/outcomeA total of 11,532 breath sound files were labeled, with 579 doubtful labels identified. After relabeling and exclusion, there were 305 labels with gold standard. For wheezing, both human physicians and the AI model demonstrated good sensitivities (89.5% vs. 86.0%) and good specificities (96.4% vs. 95.2%). For crackles, both human physicians and the AI model showed good sensitivities (93.9% vs. 80.3%) but poor specificities (56.6% vs. 65.9%). Lower AUROC values were noted in crackles identification for both physicians and the AI model compared to wheezing.ConclusionEven with the assistance of artificial intelligence tools, accurately identifying crackles compared to wheezing remains challenging. Consequently, crackles are unreliable for medical decision-making, and further examination is warranted.

Behavioral Nudging With Generative AI for Content Development in SMS Health Care Interventions: Case Study.

Brief message interventions have demonstrated immense promise in health care, yet the development of these messages has suffered from a dearth of transparency and a scarcity of publicly accessible data sets. Moreover, the researcher-driven content creation process has raised resource allocation issues, necessitating a more efficient and transparent approach to content development. This research sets out to address the challenges of content development for SMS interventions by showcasing the use of generative artificial intelligence (AI) as a tool for content creation, transparently explaining the prompt design and content generation process, and providing the largest publicly available data set of brief messages and source code for future replication of our process. Leveraging the pretrained large language model GPT-3.5 (OpenAI), we generate a collection of messages in the context of medication adherence for individuals with type 2 diabetes using evidence-derived behavior change techniques identified in a prior systematic review. We create an attributed prompt designed to adhere to content (readability and tone) and SMS (character count and encoder type) standards while encouraging message variability to reflect differences in behavior change techniques. We deliver the most extensive repository of brief messages for a singular health care intervention and the first library of messages crafted with generative AI. In total, our method yields a data set comprising 1150 messages, with 89.91% (n=1034) meeting character length requirements and 80.7% (n=928) meeting readability requirements. Furthermore, our analysis reveals that all messages exhibit diversity comparable to an existing publicly available data set created under the same theoretical framework for a similar setting. This research provides a novel approach to content creation for health care interventions using state-of-the-art generative AI tools. Future research is needed to assess the generated content for ethical, safety, and research standards, as well as to determine whether the intervention is successful in improving the target behaviors.

Tools of Artificial Intelligence Technology as a Framework for Transformation Digital Marketing Communication

The article researches the transformative possibility of Artificial Intelligence (AI) technology tools in digital marketing communications. By leveraging machine learning, natural language processing, and data analytics capabilities, AI tools enable marketers to automate tasks, personalize content, optimize campaigns, and gain deeper insights into customer behaviour. The paper examines applications of AI in digital marketing, analyses the way of transforming digital communication by AI, compares it to the traditional PESO communications model, presents the utilization of AI tools within the PESO framework and their collaboration to enhance communications, and proposes a framework for successful implementation. The research question in this paper is: how do artificial intelligence tools transform communication in a digital marketing environment? The results provide valuable insights into how AI-based technology transforming digital marketing communications.

Using Generative AI to Promote Psychological, Feedback, and Artificial Intelligence Literacies in Undergraduate Psychology

Background With the arrival of generative artificial intelligence (genAI) tools, psychology educators are rethinking their assessment practices. Objective This paper describes one approach to integrating genAI into an assessment designed to promote psychological literacy. Method Students used ChatGPT to generate a media release about a published article and then wrote a critique. We evaluated whether students were able to use the marking rubric to assess the ChatGPT output, and whether working with the rubric early in the assessment process had benefits for their grades on subsequent tasks. Results The results show that students accurately assessed the ChatGPT output against the marking rubric, judging the output to be stylistically good but lacking in accurate coverage of the aims, methods, and results of the research. Working with genAI and the marking rubric early in the assessment process had benefits for performance, relative to cohorts that had engaged in peer review. Conclusion By allowing students to use genAI and scaffolding the process of critiquing and revising, students gained competencies in psychological, feedback, and AI literacies. Teaching implications Integrating genAI presents opportunities for learning, if educators can think beyond the artifact and design assessment that allows our students to showcase their learning process.

Potential for GenAI in the Public Domain: A Review of Transportation, Healthcare, Agriculture, and Law

Generative Artificial Intelligence (GenAI) tools are becoming quite popular for a variety of operations. One such tool, ChatGPT, is rapidly permeating into people's daily lives and is considered to have the potential to reshape our society. While private organizations are spending huge amounts of money on ChatGPT, its usage in the public domain is still driven by its open access and simple functionality. This study draws on the key concepts of ‘Effective Use’ theory: Transparent Interaction, Representational Fidelity, Informed Action, and Learning and Adaptation to examine ChatGPT's current state of diffusion in four public sector domains: transportation, healthcare, agriculture, and law. We find transparent interaction is better in transportation, agriculture, and law than healthcare; representational fidelity presents a complex picture whereas informed action has been positive across domains; and learning and adaptation is an ongoing need. We conclude with various suggestions related to research and policy towards boosting GenAI's adoption. We suggest that governments invest resources and develop new regulatory frameworks considering the specific context and use cases for leveraging the enormous potential of GenAI tools in the public domain.

The continuous improvement of digital assistance in the radiation oncologist's work: from web-based nomograms to the adoption of large-language models (LLMs). A systematic review by the young group of the Italian association of radiotherapy and clinical oncology (AIRO).

Recently, the availability of online medical resources for radiation oncologists and trainees has significantly expanded, alongside the development of numerous artificial intelligence (AI)-based tools. This review evaluates the impact of web-based clinical decision-making tools in the clinical practice of radiation oncology. We searched databases, including PubMed, EMBASE, and Scopus, using keywords related to web-based clinical decision-making tools and radiation oncology, adhering to PRISMA guidelines. Out of 2161 identified manuscripts, 70 were ultimately included in our study. These papers all supported the evidence that web-based tools can be transversally integrated into multiple radiation oncology fields, with online applications available for dose and clinical calculations, staging and other multipurpose intents. Specifically, the possible benefit of web-based nomograms for educational purposes was investigated in 35 of the evaluated manuscripts. As regards to the applications of digital and AI-based tools to treatment planning, diagnosis, treatment strategy selection and follow-up adoption, a total of 35 articles were selected. More specifically, 19 articles investigated the role of these tools in heterogeneous cancer types, while nine and seven articles were related to breast and head & neck cancers, respectively. Our analysis suggests that employing web-based and AI tools offers promising potential to enhance the personalization of cancer treatment.

The effect of artificial intelligence tools on EFL learners' engagement, enjoyment, and motivation

The use of Artificial Intelligence (AI) tools has become increasingly prevalent within higher education and numerous studies have indicated the efficacy of AI applications within the English as a foreign language (EFL) context. Enhancing student engagement, foreign language enjoyment (FLE), and motivation in the field of AI education are significant in promoting effective learning outcomes. The rise of AI in educational settings has led to a paucity of research exploring its impact on various educational constructs. This quasi-experimental research examined the role of utilizing an AI tool, specifically Duolingo, on the engagement of FLE, and motivation of Chinese EFL learners. The present study involved 383 out of 412 Chinese university EFL students, who were assigned to either the experimental or control group. Both groups participated in pre- and post-intervention assessments prior to and following a 12-session intervention. Running a One-way Analysis of Covariance (ANCOVA) revealed statistically significant improvements in FLE, engagement, and motivation within the experimental group. On the contrary, the control group demonstrated a minor alteration in the aforementioned variables. AI tools possess the capability to captivate the attention of students and consequently enhance their motivation to actively participate in the educational experience. The results of the study designate that there are significant implications for both teachers and teacher educators concerning the incorporation of AI tools in the EFL classroom environment.

Enhanced Input-Doubling Method Leveraging Response Surface Linearization to Improve Classification Accuracy in Small Medical Data Processing

Currently, the tasks of intelligent data analysis in medicine are becoming increasingly common. Existing artificial intelligence tools provide high effectiveness in solving these tasks when analyzing sufficiently large datasets. However, when there is very little training data available, current machine learning methods do not ensure adequate classification accuracy or may even produce inadequate results. This paper presents an enhanced input-doubling method for classification tasks in the case of limited data analysis, achieved via expanding the number of independent attributes in the augmented dataset with probabilities of belonging to each class of the task. The authors have developed an algorithmic implementation of the improved method using two Naïve Bayes classifiers. The method was modeled on a small dataset for cardiovascular risk assessment. The authors explored two options for the combined use of Naïve Bayes classifiers at both stages of the method. It was found that using different methods at both stages potentially enhances the accuracy of the classification task. The results of the improved method were compared with a range of existing methods used for solving the task. It was demonstrated that the improved input-doubling method achieved the highest classification accuracy based on various performance indicators.

Use of AI in Mental Health Care: Community and Mental Health Professionals Survey.

Artificial intelligence (AI) has been increasingly recognized as a potential solution to address mental health service challenges by automating tasks and providing new forms of support. This study is the first in a series which aims to estimate the current rates of AI technology use as well as perceived benefits, harms, and risks experienced by community members (CMs) and mental health professionals (MHPs). This study involved 2 web-based surveys conducted in Australia. The surveys collected data on demographics, technology comfort, attitudes toward AI, specific AI use cases, and experiences of benefits and harms from AI use. Descriptive statistics were calculated, and thematic analysis of open-ended responses were conducted. The final sample consisted of 107 CMs and 86 MHPs. General attitudes toward AI varied, with CMs reporting neutral and MHPs reporting more positive attitudes. Regarding AI usage, 28% (30/108) of CMs used AI, primarily for quick support (18/30, 60%) and as a personal therapist (14/30, 47%). Among MHPs, 43% (37/86) used AI; mostly for research (24/37, 65%) and report writing (20/37, 54%). While the majority found AI to be generally beneficial (23/30, 77% of CMs and 34/37, 92% of MHPs), specific harms and concerns were experienced by 47% (14/30) of CMs and 51% (19/37) of MHPs. There was an equal mix of positive and negative sentiment toward the future of AI in mental health care in open feedback. Commercial AI tools are increasingly being used by CMs and MHPs. Respondents believe AI will offer future advantages for mental health care in terms of accessibility, cost reduction, personalization, and work efficiency. However, they were equally concerned about reducing human connection, ethics, privacy and regulation, medical errors, potential for misuse, and data security. Despite the immense potential, integration into mental health systems must be approached with caution, addressing legal and ethical concerns while developing safeguards to mitigate potential harms. Future surveys are planned to track use and acceptability of AI and associated issues over time.

Perspectives and experiences of health sciences academics regarding ChatGPT: A qualitative study

Purpose This study aimed to explore the perspectives and experiences of healthcare academics regarding the impact of ChatGPT, an artificial intelligence (AI)-supported language model, on education and research. Sample and methods This qualitative study employed a phenomenological analysis approach. The study sample consisted of nine academics from the Faculty of Health Sciences at a university in Türkiye, selected through purposive sampling method. Data were collected through semi-structured interviews, coded using the MAXQDA software, and analyzed using content analysis. Results The participants highlighted that while ChatGPT offers rapid access to information, it occasionally fails to provide current and accurate data. They also noted that the students’ misuse of ChatGPT for assignments and exams has a negative effect on their critical thinking and information retrieval skills. The academics reported that there is a need for expert oversight and verification of the data generated by ChatGPT. Conclusion While ChatGPT offers significant benefits such as enhanced efficiency in academic research and education, it also presents challenges, including accuracy and ethical concerns. Institutions should integrate ChatGPT with clear guidelines to maximize its benefits while maintaining academic integrity. Future studies should explore the long-term impacts of AI tools, such as ChatGPT, on educational outcomes and their application across various disciplines.

Generative AI in Science Education: A Learning Revolution or a Threat to Academic Integrity? A Bibliometric Analysis

The integration of generative artificial intelligence (AI) in Science, Technology, Engineering, and Mathematics (STEM) education presents transformative opportunities alongside significant challenges. This study investigates the dual impact of generative AI on STEM learning outcomes and academic integrity through a comprehensive bibliometric analysis employing co-citation, keyword analysis, and trend mapping. The results reveal that AI tools such as ChatGPT have revolutionized personalized learning by offering tailored feedback, enhancing critical thinking, and improving student engagement. However, these advancements are tempered by concerns over academic misconduct, particularly plagiarism, and the erosion of essential cognitive skills due to overreliance on AI-generated content. Ethical considerations remain critical, necessitating the development of robust policies and ethical frameworks to safeguard academic integrity. Beyond educational settings, the findings suggest broader applicability to professional training and skills development, as the benefits and challenges of AI extend beyond coursework. This research provides valuable insights for educators, policymakers, and researchers, advocating for a balanced approach to AI integration that maximizes its potential while preserving educational standards.

ORGANISATIONAL CREATIVITY AND ARTIFICIAL INTELLIGENCE: INTRODUCTION OF A CREATIVITY ASSESSMENT FOR IDEATION

The assessment of creativity is both elaborating and challenging with today’s tools and methods in organisational creativity. This study presents the result of an AI-based model for the originality assessment of creative ideas using an artificial intelligence (AI) tool (Word2Vec). First, we define everyday creativity and outline the relevance and challenges of its evaluation. Then, the Word2Vec algorithm is introduced and used to develop three different models for originality assessment by calculating a semantic distance between ideas. We conclude with an empirical validation of the three models. While results indicate that all three models can be used to measure idea originality, they further point out that merely human-based assessments may be task-dependent. The paper closes with implications for research and practice to integrate AI in creative idea originality measurement. Managers can use the methods presented in this study to benefit from AI-supported creativity within their companies. Furthermore, idea managers of companies with large idea databases can implement the algorithms to analyse employee ideas regarding specific contexts or topics within their innovation management processes.

Artificial Intelligence for Optimizing Cancer Imaging: User Experience Study.

The need for increased clinical efficacy and efficiency has been the main force in developing artificial intelligence (AI) tools in medical imaging. The INCISIVE project is a European Union-funded initiative aiming to revolutionize cancer imaging methods using AI technology. It seeks to address limitations in imaging techniques by developing an AI-based toolbox that improves accuracy, specificity, sensitivity, interpretability, and cost-effectiveness. To ensure the successful implementation of the INCISIVE AI service, a study was conducted to understand the needs, challenges, and expectations of health care professionals (HCPs) regarding the proposed toolbox and any potential implementation barriers. A mixed methods study consisting of 2 phases was conducted. Phase 1 involved user experience (UX) design workshops with users of the INCISIVE AI toolbox. Phase 2 involved a Delphi study conducted through a series of sequential questionnaires. To recruit, a purposive sampling strategy based on the project's consortium network was used. In total, 16 HCPs from Serbia, Italy, Greece, Cyprus, Spain, and the United Kingdom participated in the UX design workshops and 12 completed the Delphi study. Descriptive statistics were performed using SPSS (IBM Corp), enabling the calculation of mean rank scores of the Delphi study's lists. The qualitative data collected via the UX design workshops was analyzed using NVivo (version 12; Lumivero) software. The workshops facilitated brainstorming and identification of the INCISIVE AI toolbox's desired features and implementation barriers. Subsequently, the Delphi study was instrumental in ranking these features, showing a strong consensus among HCPs (W=0.741, P<.001). Additionally, this study also identified implementation barriers, revealing a strong consensus among HCPs (W=0.705, P<.001). Key findings indicated that the INCISIVE AI toolbox could assist in areas such as misdiagnosis, overdiagnosis, delays in diagnosis, detection of minor lesions, decision-making in disagreement, treatment allocation, disease prognosis, prediction, treatment response prediction, and care integration throughout the patient journey. Limited resources, lack of organizational and managerial support, and data entry variability were some of the identified barriers. HCPs also had an explicit interest in AI explainability, desiring feature relevance explanations or a combination of feature relevance and visual explanations within the toolbox. The results provide a thorough examination of the INCISIVE AI toolbox's design elements as required by the end users and potential barriers to its implementation, thus guiding the design and implementation of the INCISIVE technology. The outcome offers information about the degree of AI explainability required of the INCISIVE AI toolbox across the three services: (1) initial diagnosis; (2) disease staging, differentiation, and characterization; and (3) treatment and follow-up indicated for the toolbox. By considering the perspective of end users, INCISIVE aims to develop a solution that effectively meets their needs and drives adoption.

A Novel Cognitive Behavioral Therapy-Based Generative AI Tool (Socrates 2.0) to Facilitate Socratic Dialogue: Protocol for a Mixed Methods Feasibility Study.

Digital mental health tools, designed to augment traditional mental health treatments, are becoming increasingly important due to a wide range of barriers to accessing mental health care, including a growing shortage of clinicians. Most existing tools use rule-based algorithms, often leading to interactions that feel unnatural compared with human therapists. Large language models (LLMs) offer a solution for the development of more natural, engaging digital tools. In this paper, we detail the development of Socrates 2.0, which was designed to engage users in Socratic dialogue surrounding unrealistic or unhelpful beliefs, a core technique in cognitive behavioral therapies. The multiagent LLM-based tool features an artificial intelligence (AI) therapist, Socrates, which receives automated feedback from an AI supervisor and an AI rater. The combination of multiple agents appeared to help address common LLM issues such as looping, and it improved the overall dialogue experience. Initial user feedback from individuals with lived experiences of mental health problems as well as cognitive behavioral therapists has been positive. Moreover, tests in approximately 500 scenarios showed that Socrates 2.0 engaged in harmful responses in under 1% of cases, with the AI supervisor promptly correcting the dialogue each time. However, formal feasibility studies with potential end users are needed. This mixed methods study examines the feasibility of Socrates 2.0. On the basis of the initial data, we devised a formal feasibility study of Socrates 2.0 to gather qualitative and quantitative data about users' and clinicians' experience of interacting with the tool. Using a mixed method approach, the goal is to gather feasibility and acceptability data from 100 users and 50 clinicians to inform the eventual implementation of generative AI tools, such as Socrates 2.0, in mental health treatment. We designed this study to better understand how users and clinicians interact with the tool, including the frequency, length, and time of interactions, users' satisfaction with the tool overall, quality of each dialogue and individual responses, as well as ways in which the tool should be improved before it is used in efficacy trials. Descriptive and inferential analyses will be performed on data from validated usability measures. Thematic analysis will be performed on the qualitative data. Recruitment will begin in February 2024 and is expected to conclude by February 2025. As of September 25, 2024, overall, 55 participants have been recruited. The development of Socrates 2.0 and the outlined feasibility study are important first steps in applying generative AI to mental health treatment delivery and lay the foundation for formal feasibility studies. DERR1-10.2196/58195.

AI empowered academia: a fuzzy prioritization framework for academic challenges

Purpose The rapid expansion of artificial intelligence (AI) is progressively reshaping the dynamics of human interaction, communication, lifestyle, education and professional endeavors. The purpose of the study is to comprehend and address the barriers which are impeding the implementation of Generative AI Technologies, such as ChatGPT in the educational landscape. Design/methodology/approach The study used the Fuzzy analytic hierarchy process (AHP) model to analyze the responses gathered from 149 academicians belonging to the northern states of India. Findings The study established that the three most important criteria that influence the adoption of generative AI in the education sector are Risk of Academic Integrity, Risk of biased outcomes and Erosion of Critical Thinking. Research limitations/implications The present study was confined to Fuzzy AHP to extract the critical criteria influencing the decision-making. Various other techniques such as PF-Delphi and PF-CoCoSo can be used further. The results provide significant inputs for future research to understand the effect of adoption of Generative AI in different contexts including both opportunities and the challenges faced by them. Practical implications The study will be beneficial to various stakeholders including students, educators, society and policymakers as the study will highlight the importance of AI tools, introduce the various challenges associated with and explain the use of these tools as productivity-enhancing tools. Originality/value To the best of the author’s knowledge, the present study is a novice as the use of AI in Academia is unexplored and the major criteria influencing the choices have yet been undiscovered.