Artificial Intelligence Tools Research Articles

The potential of ChatGPT technology in education: advantages, obstacles and future growth

<span lang="EN-US">Information and communication technology is becoming increasingly prevalent in our daily lives, with interactive communication modes such as social networks and instant messaging reaching unprecedented popularity. These tools are now widely utilised in various academic and research institutions by both faculty and students for communication and distance learning purposes. <a name="_Hlk155360394"></a>Chat generative pre-trained transformer (ChatGPT) and artificial intelligence tools hold the potential to revolutionise the way that students obtain knowledge and support. ChatGPT is a cutting-edge language technology capable of constructing intelligent, coherent texts, making it a valuable tool for writing and communication across different fields, including education. However, universities that incorporate ChatGPT as a teaching tool must address concerns regarding plagiarism and academic integrity. This investigation focuses on the advantages and obstacles of applying ChatGPT technology in the education field and its potential for future development. Findings reveal that through careful consideration of the ethical dilemmas and issues, academic institutions can leverage the maximum potential of ChatGPT to provide a more accessible, successful, and personalised learning experience for learners. The development prospects for ChatGPT appear promising, given its potential to grow and enhance its capabilities through on-going research and innovation.</span>

A Review on Artificial Intelligence in Pharmaceutical Science

Abstract: In recent years, the use of artificial intelligence (AI) in health care has risen steadily, including a wide range of applications in the field of pharmacology. AI is now used throughout the entire continuum of pharmacology research and clinical practice and from early drug discovery to real-world data mining. The types of AI models used range from unsupervised clustering of drugs or patients aimed at identifying potential drug compounds or suitable patient populations, to supervised machine learning approaches to improve therapeutic drug monitoring. Additionally, natural language processing is increasingly used to mine electronic health records to obtain real-world data. In this mini-review, we discuss the basics of AI followed by an outline of its application in pharmacology research and clinical practice. Artificial intelligence is the upcoming technology in advance health care system. Current digitalization of medicine and availability of electronic health records (EHRs) has inspired clinical researchers and healthcare personnel to acquire artificial intelligence (AI) methodologies for big data analytics and for very large scale medical databases. The major advantage of AI is that it reduces the time that is needed for drug development and, in turn, it reduces the costs that are associated with drug development, enhances the returns on investment and may even cause a decrease in cost for the end user. A large number of researches are being carried out to improve the current available AI technology to make the pharmacy profession more efficient. The present article briefly describes the importance of AI in the process of drug development and then looks at the various AI tools that are available at the disposal of a modern-day pharmacist to aid in a more efficient functioning.

The role of student motivation in integrating AI into web design education: A longitudinal study

Amidst the current wave studies of artificial intelligence (AI) in education, this longitudinal case study, spanning Spring 2023 to Spring 2024, delves into the integration of AI in the UI/UX web design classroom. By introducing both text-based and image-based AI tools to students with varying levels of skill in introductory web design and user experience (UX) courses, the study observed a significant enhancement in student creative capabilities and project outcomes. The utilization of text-based generators markedly improved writing efficiency and coding, while image-based tools facilitated better ideation and color selection. These findings underscore the potential to augment traditional educational methods, providing students with novel avenues for creativity and innovation. At the same time, the goal of this study was also to ascertain the factors that led to the adoption of AI tools in the educational workflow, specifically focusing on student major and background, thereby illuminating how AI can be tailored to meet diverse educational needs and foster a more adaptive and innovative learning environment. The findings reveal that students were more receptive to integrating AI tasks into their workflows when these tasks did not directly relate to their major field of study. For example, Computer Science students exhibited less resistance to using AI for selecting color palettes, a task outside their primary focus, compared to utilizing AI for coding. Additionally, the study observed a significant growth in both awareness and usage of AI tools among students throughout the duration of the research. This trend suggests an increasing incorporation of AI technologies into their standard toolkit, highlighting a broader acceptance and integration of AI in educational practices.

The Ethical Stewardship of Artificial Intelligence in Chronic Pain and Headache: A Narrative Review.

As artificial intelligence (AI) and machine learning (ML) are becoming more pervasive in medicine, understanding their ethical considerations for chronic pain and headache management is crucial for optimizing their safety. We reviewed thirty-eight editorial and original research articles published between 2018 and 2023, focusing on the application of AI and ML to chronic pain or headache. The core medical principles of beneficence, non-maleficence, autonomy, and justice constituted the evaluation framework. The AI applications addressed topics such as pain intensity prediction, diagnostic aides, risk assessment for medication misuse, empowering patients to self-manage their conditions, and optimizing access to care. Virtually all AI applications aligned both positively and negatively with specific medical ethics principles. This review highlights the potential of AI to enhance patient outcomes and physicians' experiences in managing chronic pain and headache. We emphasize the importance of carefully considering the advantages, disadvantages, and unintended consequences of utilizing AI tools in chronic pain and headache, and propose the four core principles of medical ethics as an evaluation framework.

What distance learning students want from an AI Digital Assistant

With the release of Generative AI systems such as ChatGPT, an increasing interest in using Artificial Intelligence (AI) has been observed across domains, including higher education. While emerging statistics show the popularity of using AI amongst undergraduate students, little is yet known about students’ perceptions regarding AI including self-reported benefits and concerns from their actual usage, in particular in distance learning contexts. Using a two-step, mixed-methods approach, we examined the perceptions of ten online and distance learning students from diverse disciplines regarding the design of a hypothetical AI Digital Assistant (AIDA). In the first step, we captured students’ perceptions via interviews, while the second step supported the triangulation of data by enabling students to share, compare, and contrast perceptions with those of peers. All participants agreed on the usefulness of such an AI tool while studying and reported benefits from using it for real-time assistance and query resolution, support for academic tasks, personalization and accessibility, together with emotional and social support. Students concerns related to the ethical and social implications of implementing AIDA, data privacy and data use, operational challenges, academic integrity and misuse, and the future of education. Implications for the design of AI-tailored systems are also discussed.

ChatGPT in public policy teaching and assessment: An examination of opportunities and challenges

AbstractThis paper presents the findings of an innovative assessment task that required students to use ChatGPT for drafting a policy brief to an Australian Government minister. The study explores how future public policy students perceive ChatGPT's role in both public policy and teaching and assessment. Through self‐reflective essays and focus group discussions, the research looks at the limitations of ChatGPT that the students identified, demonstrating it struggles to produce analytically sound, politically responsive, and nuanced policy recommendations. The findings align with the “technoscepticism” theoretical frame, indicating concerns that artificial intelligence (AI) tools could undermine good policy analysis processes. The students supported greater use of ChatGPT in the classroom, to increase ChatGPT‐literacy, help students learn to engage ethically and appropriately with AI tools, and better develop evaluative judgement skills.The paper contributes insights into the intersection of ChatGPT, teaching and assessment, and public policy and seeks to prompt further exploration and discussion on the implications of integrating ChatGPT into both public policy and its education and assessment.Points for practitioners Future public service graduates are highly sceptical about the value of ChatGPT for developing policy. They are concerned about the ethical implications, the lack of transparency, and the impact it may have on marginalised communities.

Classification of artificial intelligence tools for civil engineering under the notion of complex fuzzy rough Frank aggregation operators

In recent days researchers have tried to handle the maximum information and use those techniques and methods in which there is no chance of data loss or loss of information is minimum. The structure like fuzzy set and complex fussy set cannot discuss the upper and lower approximations. Moreover, we can observe that a fuzzy rough set cannot discuss the second dimension and in this case, there is a chance of data loss. To cover all these issues in previous ideas, the notion of a complex fuzzy rough set in Cartesian form is the demand of the day because this structure can discuss the second dimension as well as upper and lower approximations. For this purpose, in this manuscript, we have developed the theory of complex fuzzy relation and complex fuzzy rough set in Cartesian form. Moreover, we have initiated the fundamental laws for complex fuzzy rough numbers based on Frank t-norm and t-conorm. The fundamental tools that can convert the overall input into a single output are called aggregation operators (AOs). So based on the characteristics of AOs, we have defined the notion of complex fuzzy rough Frank average and complex fuzzy rough Frank geometric AOs. The utilization of the developed theory is necessary to show the importance and validity of the delivered approach. So based on developed notions, we have defined an algorithm for this purpose along with an illustrative example. We have utilized the introduced structure for the classification of AI tools for civil engineering. Moreover, the comparative analysis of the delivered approach shows the advancement of the introduced structure as compared to existing notions.

AI for interpreting screening mammograms: implications for missed cancer in double reading practices and challenging-to-locate lesions

Although the value of adding AI as a surrogate second reader in various scenarios has been investigated, it is unknown whether implementing an AI tool within double reading practice would capture additional subtle cancers missed by both radiologists who independently assessed the mammograms. This paper assesses the effectiveness of two state-of-the-art Artificial Intelligence (AI) models in detecting retrospectively-identified missed cancers within a screening program employing double reading practices. The study also explores the agreement between AI and radiologists in locating the lesions, considering various levels of concordance among the radiologists in locating the lesions. The Globally-aware Multiple Instance Classifier (GMIC) and Global–Local Activation Maps (GLAM) models were fine-tuned for our dataset. We evaluated the sensitivity of both models on missed cancers retrospectively identified by a panel of three radiologists who reviewed prior examinations of 729 cancer cases detected in a screening program with double reading practice. Two of these experts annotated the lesions, and based on their concordance levels, cases were categorized as 'almost perfect,' 'substantial,' 'moderate,' and 'poor.' We employed Similarity or Histogram Intersection (SIM) and Kullback–Leibler Divergence (KLD) metrics to compare saliency maps of malignant cases from the AI model with annotations from radiologists in each category. In total, 24.82% of cancers were labeled as “missed.” The performance of GMIC and GLAM on the missed cancer cases was 82.98% and 79.79%, respectively, while for the true screen-detected cancers, the performances were 89.54% and 87.25%, respectively (p-values for the difference in sensitivity < 0.05). As anticipated, SIM and KLD from saliency maps were best in ‘almost perfect,’ followed by ‘substantial,’ ‘moderate,’ and ‘poor.’ Both GMIC and GLAM (p-values < 0.05) exhibited greater sensitivity at higher concordance. Even in a screening program with independent double reading, adding AI could potentially identify missed cancers. However, the challenging-to-locate lesions for radiologists impose a similar challenge for AI.

Treading water: new data on the impact of AI ethics information sessions in classics and ancient language pedagogy

Abstract Over 2023, many universities and policy organisations in the higher education (HE) sector are working to create guiding principles and guidelines for the use of generative artificial intelligence (AI) in HE Teaching and Learning (T&amp;L). Despite these guidelines, students remain unsure if and how they should use AI. This article discusses the AI information sessions held over the Autumn 2023 term in the Department of Classics at the University of Reading, which aimed to provide students with the knowledge and tools to make informed judgements about using AI in their studies. These sessions discussed the benefits and drawbacks of generative AI, highlighting training data, content policy, environmental impact, and examples of potential uses. Staff and student participants were surveyed before and after these information sessions to gather their opinions surrounding AI use. Although at least 60% of participants had previously used generative AI, 80% of participants were apprehensive of or against using generative AI tools for learning purposes following the AI information sessions. By providing staff and students with the ethical considerations surrounding generative AI, they can make an informed judgement about using AI in their work without misplaced faith or excessive fear.

Artificial versus natural intelligence: Overcoming students' cheating likelihood with artificial intelligence tools during virtual assessment

AbstractAssessment techniques need to evolve beyond traditional methods in light of the rapidly developing artificial intelligence (AI) tool technologies, such as Copilot, Bard, and ChatGPT. These AI‐powered Chatbot is designed to appear similar to human speech or text and present information conversationally, making them tenable options for student assessment support worldwide. Consequently, to take advantage of the weaknesses in the AI system and foster a creative attitude in their pupils, educators must reconsider their approach to evaluation. The study conducts a comparative experiment on two different assessment methods—the traditional questioning strategy (Experiment I) versus the alternative or modified strategy (Experiment II), to assess how well the AI tools perform in the assessment and how the new technique can deter students from engaging in academic dishonesty. According to the study in Experiment I, the AI‐Chatbot had a 100% positive response correlation, but in Experiment II, it had a shockingly low positive response correlation. Comparably, pupils who use AI‐Chatbot and those who do not have significant performance disparities (α = 0.05, p‐value &lt; 0.001; 1.8331). Inferentially, AI‐Chatbot helped students a lot in Experiment I but did considerably less in Experiment II. In other words, Experiment II's questioning approach outperforms the AI tools' level of competence. The study comes to the conclusion that if AI is effectively harnessed, human natural intelligence will always be able to overcome the challenges posed by these powerful AI technologies.

The intelligent Impella: Future perspectives of artificial intelligence in the setting of Impella support.

Artificial intelligence (AI) has emerged as a potential useful tool to support clinical treatment of heart failure, including the setting of mechanical circulatory support (MCS). Modern Impella pumps are equipped with advanced technology (SmartAssist), enabling real-time acquisition and display of data related to both pump performance and the patient's haemodynamic status. These data emerge as an 'ideal' source for data-driven AI applications to predict the clinical course of an ongoing therapeutic protocol. Yet, no evidence of effective application of AI tools in the setting of Impella support is available. On this background, we aimed at identifying possible future applications of AI-based tools in the setting of temporary MCS with an Impella device. We explored the state of research and development at the intersection of AI and Impella support and derived future potential applications of AI in routine Impella clinical management. We identified different areas where the future implementation of AI tools may contribute to addressing important clinical challenges in the setting of Impella support, including (i) early identification of the best suited pathway of care according to patients' conditions at presentation and intention to treat, (ii) prediction of therapy outcomes according to different possible therapeutic actions, (iii) optimization of device implantation procedures and evaluation of proper pump position over the whole course of support and (iv) prevention and/or rationale management of haemocompatibility-related adverse events. For each of those areas, we discuss the potential advantages, challenges and implications of harnessing AI-driven insights in the setting of MCS with an Impella device. Temporary MCS with an Impella device has great potential to benefit from the integration of AI-based tools. Such tools may indeed translate into groundbreaking innovation supporting clinical decision-making and therapy regulation, in particular in complex scenarios such as the multidevice MCS strategy.

Enhancing clinical skills in pediatric trainees: a comparative study of ChatGPT-assisted and traditional teaching methods

BackgroundAs artificial intelligence (AI) increasingly integrates into medical education, its specific impact on the development of clinical skills among pediatric trainees needs detailed investigation. Pediatric training presents unique challenges which AI tools like ChatGPT may be well-suited to address.ObjectiveThis study evaluates the effectiveness of ChatGPT-assisted instruction versus traditional teaching methods on pediatric trainees’ clinical skills performance.MethodsA cohort of pediatric trainees (n = 77) was randomly assigned to two groups; one underwent ChatGPT-assisted training, while the other received conventional instruction over a period of two weeks. Performance was assessed using theoretical knowledge exams and Mini-Clinical Evaluation Exercises (Mini-CEX), with particular attention to professional conduct, clinical judgment, patient communication, and overall clinical skills. Trainees’ acceptance and satisfaction with the AI-assisted method were evaluated through a structured survey.ResultsBoth groups performed similarly in theoretical exams, indicating no significant difference (p > 0.05). However, the ChatGPT-assisted group showed a statistically significant improvement in Mini-CEX scores (p < 0.05), particularly in patient communication and clinical judgment. The AI-teaching approach received positive feedback from the majority of trainees, highlighting the perceived benefits in interactive learning and skill acquisition.ConclusionChatGPT-assisted instruction did not affect theoretical knowledge acquisition but did enhance practical clinical skills among pediatric trainees. The positive reception of the AI-based method suggests that it has the potential to complement and augment traditional training approaches in pediatric education. These promising results warrant further exploration into the broader applications of AI in medical education scenarios.

General Review of Smartphone Usability in Noise Mapping

The global urban population is steadily increasing, with more than half of the world's population currently residing in cities, and this trend is expected to double by 2050. As urbanization continues, noise pollution becomes a significant concern, affecting over 60% of major city dwellers and impacting human health on both physiological and psychological levels. To address this issue, governments and organizations are striving to develop effective noise assessment, regulation, and mitigation policies. This literature review explores the role of noise mapping and the potential of smartphones in collecting noise data to inform these policies. Traditional noise mapping techniques and smartphone-based data collection methods are discussed, along with their importance in urban planning, environmental studies, and public health. Key research questions are identified, including the methodologies employed for smartphone-based noise mapping, the accuracy of smartphone-collected data compared to traditional measurements, practical applications, challenges, and emerging trends. The review reveals that smartphones offer a cost-effective and widespread means of gathering noise data, enabling real-time insights and enhancing various domains' practical applications. However, challenges such as data accuracy, privacy concerns, and device limitations must be addressed. The future of smartphone-based noise mapping looks promising, with advancements in sensor technologies, artificial intelligence, and data analysis tools empowering researchers, urban planners, and policymakers to make informed decisions about noise pollution in urban environments.

Multi-fuzzy sets and neural networks: a collaborative tool for artificial intelligence

Multi-fuzzy sets and neural networks: a collaborative tool for artificial intelligence

Deep Learning for Breast Cancer Risk Prediction: Application to a Large Representative UK Screening Cohort.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop an artificial intelligence (AI) deep learning tool capable of predicting future breast cancer risk from a current negative screening mammographic examination and to evaluate the model on data from the UK National Health Service Breast Screening Program. Materials and Methods The OPTIMAM Mammography Imaging Database contains screening data, including mammograms and information on interval cancers, for > 300,000 women who attended screening at three different sites in the UK from 2012 onward. Cancer-free screening examinations from women aged 50-70 years were obtained and classified as risk-positive or risk-negative based on the occurrence of cancer within 3 years of the original examination. Examinations with confirmed cancer and images containing implants were excluded. From the resulting 5264 risk-positive and 191488 risk-negative examinations, training (n = 89285) validation (n = 2106) and test (n = 39351) datasets were produced for model development and evaluation. The AI model was trained to predict future cancer occurrence based on screening mammograms and patient age. Performance was evaluated on the test dataset using the area under the receiver operating characteristic curve (AUC) and compared across subpopulations to assess potential biases. Interpretability of the model was explored, including with saliency maps. Results On the hold-out test set, the AI model achieved an overall AUC of 0.70 (95% CI: 0.69, 0.72). There was no evidence of a difference in performance across the three sites, between patient ethnicities or across age-groups Visualization of saliency maps and sample images provided insights into the mammographic features associated with AI-predicted cancer risk. Conclusion The developed AI tool showed good performance on a multisite, UK-specific dataset. ©RSNA, 2024.

Clearing a Path for Renewal and Growth

The spring 2024 issue of GATESOL Journal invites readers to consider how they traverse the path of multilingual learner education. The articles in the issue trace the historical policies that have led to the current state of education for multilingual learners, provide a checkpoint for evaluating assessment, create a plan for visualizing the destination, and explore artificial intelligence tools to help navigate the road ahead. This issue will highlight how language educators can work with and for their students to cultivate language across contexts through interrogating assessment results, reflecting on the trajectory of language policy, envisioning the future, and harnessing technology tools.

Exploring EFL Teachers’ Insights Regarding Artificial Intelligence Driven Tools in Student-Centered Writing Instructions

The significance of technology integration including artificial intelligence (AI)-mediated tools has established a notable presence in the academic spectrum. Despite the abundance of studies available on AI-mediated technology integration in writing instructions in diverse settings, there remains an apparent gap in exploring teachers&amp;rsquo; insights, particularly within the context of Arab universities. Therefore, the current study explores the employment of AI-driven tools in student-centered writing instructions from English as a Foreign Language (EFL) teachers&amp;rsquo; perspectives. Using a qualitative research methodology, this study collected data through semi-structured interviews with a sample of (N = 16) teachers from four different universities. The content analysis indicates that teachers strongly perceive a positive impact of AI writing assistants on both student involvement and the role of teachers. Additionally, they underscore the significance of professional development and the role of AI in facilitating student-centered approach for effective writing instructions. While acknowledging the efficiency, customization, and time-saving aspects of AI tools, they also expressed reservations about potential issues such as overreliance, bias, digital divide, and concerns regarding accuracy. Furthermore, the participants observed the ways to address issues and concerns associated with the integration of AI-mediated tools include, but not limited to, clear communication, ethical considerations, academic integrity, teacher roles, ongoing and latest AI updates, student-centered learning, and professional development. Finally, the study offers limitations and recommendations for future research.

Ethical Considerations of Generative AI: A Survey Exploring the Role of Decision Makers in the Loop

We explore the foresighted concerns that Norbert Wiener voiced in 1960 about the potential of machines to learn and create strategies that could not be anticipated, drawing parallels to the fable "The Sorcerer's Apprentice" by Goethe. The progress in artificial intelligence (AI) has brought these worries back to the forefront, as shown by a survey AI Impacts conducted in 2022 with more than 700 machine learning researchers. This survey found a five percentage probability that advanced AI might cause "extremely adverse" outcomes, including the possibility of human extinction. Importantly, the introduction of OpenAI's ChatGPT, powered by GPT-4, has led to a surge in entrepreneurial activities, highlighting the ease of use of large language models (LLMs).AI's potential for adverse outcomes, such as military control and unregulated AI races, is explored alongside concerns about AI's role in governance, healthcare, media portrayal, and surpassing human intelligence. Given their transformative impact on content creation, the prominence of generative AI tools such as ChatGPT is noted. The societal assessment of Artificial Intelligence (AI) has grown increasingly intricate and pressing in tandem with the rapid evolution of this technology. As AI continues to advance at a swift pace, the need to comprehensively evaluate its societal implications has become more complex and urgent, necessitating a thorough examination of its potential impact on various domains such as governance, healthcare, media portrayal, and surpassing human intelligence. This assessment is crucial in addressing ethical concerns related to bias, data misuse, technical limitations, and transparency gaps, and in integrating ethical and legal principles throughout AI algorithm lifecycles to ensure alignment with societal well-being. Furthermore, the urgency of addressing the societal implications of AI is underscored by the need for healthcare workforce upskilling and ethical considerations in the era of AI-assisted medicine, emphasizing the critical importance of integrating societal well-being into the development and deployment of AI technologies. Our study entails an examination of the ethical quandaries and obstacles presented when developing methods to evaluate and predict the broader societal impacts of AI on decision-making processes involving the generating of images, videos, and textual content.

Novel AI-based tool for primary tooth segmentation on CBCT using convolutional neural networks: A validation study.

Primary teeth segmentation on cone beam computed tomography (CBCT) scans is essential for paediatric treatment planning. Conventional methods, however, are time-consuming and necessitate advanced expertise. The aim of this study was to validate an artificial intelligence (AI) cloud-based platform for automated segmentation (AS) of primary teeth on CBCT. Its accuracy, time efficiency, and consistency were compared with manual segmentation (MS). A dataset comprising 402 primary teeth (37 CBCT scans) was retrospectively retrieved from two CBCT devices. Primary teeth were manually segmented using a cloud-based platform representing the ground truth, whereas AS was performed on the same platform. To assess the AI tool's performance, voxel- and surface-based metrics were employed to compare MS and AS methods. Additionally, segmentation time was recorded for each method, and intra-class correlation coefficient (ICC) assessed consistency between them. AS revealed high performance in segmenting primary teeth with high accuracy (98 ± 1%) and dice similarity coefficient (DSC; 95 ± 2%). Moreover, it was 35 times faster than the manual approach with an average time of 24 s. Both MS and AS demonstrated excellent consistency (ICC = 0.99 and 1, respectively). The platform demonstrated expert-level accuracy, and time-efficient and consistent segmentation of primary teeth on CBCT scans, serving treatment planning in children.

Evaluating ChatGPT's effectiveness and tendencies in Japanese internal medicine.

ChatGPT, a large-scale language model, is a notable example of AI's potential in health care. However, its effectiveness in clinical settings, especially when compared to human physicians, is not fully understood. This study evaluates ChatGPT's capabilities and limitations in answering questions for Japanese internal medicine specialists, aiming to clarify its accuracy and tendencies in both correct and incorrect responses. We utilized ChatGPT's answers on four sets of self-training questions for internal medicine specialists in Japan from 2020 to 2023. We ran three trials for each set to evaluate its overall accuracy and performance on nonimage questions. Subsequently, we categorized the questions into two groups: those ChatGPT consistently answered correctly (Confirmed Correct Answer, CCA) and those it consistently answered incorrectly (Confirmed Incorrect Answer, CIA). For these groups, we calculated the average accuracy rates and 95% confidence intervals based on the actual performance of internal medicine physicians on each question and analyzed the statistical significance between the two groups. This process was then similarly applied to the subset of nonimage CCA and CIA questions. ChatGPT's overall accuracy rate was 59.05%, increasing to 65.76% for nonimage questions. 24.87% of the questions had answers that varied between correct and incorrect in the three trials. Despite surpassing the passing threshold for nonimage questions, ChatGPT's accuracy was lower than that of human specialists. There was a significant variance in accuracy between CCA and CIA groups, with ChatGPT mirroring human physician patterns in responding to different question types. This study underscores ChatGPT's potential utility and limitations in internal medicine. While effective in some aspects, its dependence on question type and context suggests that it should supplement, not replace, professional medical judgment. Further research is needed to integrate Artificial Intelligence tools like ChatGPT more effectively into specialized medical practices.