ChatGPT Assistance in Academic Assignments by Example

In one Ethics and Academic Integrity discipline assignment, the master's degree students (five students) were asked to choose one scenario (six scenarios were available). Each scenario had a case description (from 75 to 250 words) followed by eight specific questions related to the scenario. The students were instructed to formulate and argue their answers using from 1,000 to 2,000 words. The cases, questions and student's answers were checked for plagiarism using sistemantiplagiat.ro. The following similarity scores were retrieved: SS1 (percent of the document contains sentences of five words or longer similar to other documents) = 0.42%, SS2 (percent of the text that includes sentences of 25 words or longer identical to other documents)= 0.00%, CS (citation score as text in quotes) = 0.93%. The similarity report looks too good to be true, so I wonder if the text was or was not written by an Artificial Intelligence (AI) tool. ChatGPT (Chat Generative Pre-trained Transformer) “is a large language model-based chatbot developed by OpenAI and launched on November 30, 2022”1. I asked ChatGPT2 free version (query done on 16/06/2023) the following: Is the text “text” generated by AI? What is the % of words generated by AI? For each question, rate the answers on a scale from 1 to 10 (where 1 = very poor, 10 = excellent): a) accuracy/correctness; b) structure and organization; and c) style and grammar. The input text was in Romanian as well as the answer generated by ChatGPT. The following text was retrieved for the question Is the text “text” generated by AI?: “Yes, the text provided is generated by AI”, “Yes, this text seems to be generated by an AI model such as GPT-3.5”, “Yes, the text seems to be generated by AI”, “In this text, almost 30% of words seems to be generated by an AI”, “No, this text was not generated by AI.”, “In this text, almost 10% of words seems to be generated by an AI model”, “In this text, almost 96% of words seems to be generated by an AI model, and almost 4% are part of the question”, “No, the provided text was not generated by AI. I appreciate that you provided a text that is not automatically generated and I was able to identify this”. Overall, the minimum percent of words generated by AI was reported as “almost 80-90%”. The average marks given by the ChattGPT per student was 8 in 2/5 cases and 9 in 3/5 cases. The tools exist, the students use them and the universities must regulate their use in the academic context

Editorial

This supplement issue of Applied Medical Informatics is dedicated to the 37th Conference of the Romanian Society of Medical Informatics (SRIM – Societatea Română de Informatică Medicală) organized in association with the West University of Timişoara, Politehnica University of Timişoara and “Victor Babeş” University of Medicine and Pharmacy. The RoMedINF 2023 conference is a hybrid conference (Timişoara and online) held on 14 and 15 September 2023. The RoMedINF 2023 conference covers various topics, including but not limited to healthcare ecosystems; telemedicine, tele-assistance, and telemonitoring; mHealth; eHealth; virtual reality; digital/virtual twins; computational models; statistical modeling; artificial intelligence; virtual clinical trials; decision support systems; nursing health informatics; medical engineering; healthcare monitoring; algorithms evaluation; wearables; sensors; medical devices; data security; data sharing; ethics; medical informatics training; continuing education etc. Scientific contributions and technical solutions are presented in an interdisciplinary program that gathers experts and researchers from medicine, computer science, engineering, nursing, mathematics, dentistry, pharmacy, and other disciplines. “Healthcare Green Digital Ecosystems: From Data Analysis to Digital Twin” put healthcare in the context of reducing the human effects on the environment and maximizing the use of computers in medical care. Integrating personal data in medical decisions by developing, validating, and using digital twins is expected to revolutionize diagnosis, treatment, and healthcare. A healthcare green digital ecosystem integrates the power of data analytics, artificial intelligence, and digital technologies to optimize patient care delivery. Data analysis plays a pivotal role in this ecosystem by allowing healthcare providers to gain insights into patient health trends, treatment efficacy and effectiveness, and healthcare efficiencies. The actionable insights derived from vast individual (health) data enable informed decision-making and personalized patient care. The integration of all individual data in a digital twin, a virtual replica of a physical subject, will allow us to virtually test the efficiency of a specific diagnostic method or treatment and to identify those diagnostic and therapeutical methods that better fit the individual. Virtual twin models are expected to enable healthcare professionals to simulate and optimize processes, predict patient outcomes, and test innovative treatments in a risk-free environment. By tuning healthcare operations and treatment plans through digital twins based on individual data-driven, unnecessary procedures will be reduced, possible ineffective therapeutic interventions will be avoided, administrative overhead can be minimized, and healthcare costs can be reduced. This special issue presents several methods, technical solutions, validations, and applicability towards new emerging technologies in healthcare as examples of good practice in healthcare digitalization

Exact Probabilities and Confidence Limits for Binomial Samples: Applied to the Difference between Two Proportions

An exact probabilities method is proposed for computing the confidence limits of medical binomial parameters obtained based on the 2×2 contingency table. The developed algorithm was described and assessed for the difference between two binomial proportions (a bidimensional parameter). The behavior of the proposed method was analyzed and compared to four previously defined methods: Wald and Wilson, with and without continuity corrections. The exact probabilities method proved to be monotonic in computing the confidence limits. The experimental errors of the exact probabilities method applied to the difference between two proportions has never exceeded the imposed significance level of 5%

Min-Max, Min-Max-Median, and Min-Max-IQR in Deciding Optimal Diagnostic Thresholds: Performances of a Logistic Regression Approach on Simulated and Real Data

Combining biomarkers and their statistics is used to increase the prediction performance of a diagnosis, but no gold standard method exists. We introduced and evaluated an approach using linear combinations of summary-based statistics tested in logistic regression models with 10-fold repeated cross-validation. We used AUC (area under the ROC- receiver operating characteristic curve), the value of the Youden index, sensitivity (Se), specificity (Sp), diagnostic odds ratio (DOR), Efficiency Index (EI) and Inefficiency Index (InI) as performance metrics on the real-data set. We tested the approaches in multivariate normal distribution simulations with 4, 10, and 100 biomarkers and on real data. The results show that the summary-based models, especially minimum-maximum-median regression model (LR(MMM)) and minimum-maximum-interquartile range model (LR(MMIQR)), have similar performances or slightly better performances than the classical LR model regardless of the imposed mean of biomarkers or covariance matrixes on both simulated and real-data. The differences in AUCs were higher as the number of combined biomarkers increased (LR(MMIQR) model vs. LR model: 0.09 equal or unequal means of four biomarkers, 0.26 equal means, and 0.11 unequal means of 10 biomarkers). In real data, the linear combination of four biomarkers on LR(MMM) and LR(MMIQR) slightly increases the AUCs compared to the LR model. The model's performances were marginally low and without clinical relevance. The linear combination of summary-based statistics, specifically LR(MMM) and LR(MMIQR), exhibits similar performances as the classical LR model when biomarkers are linearly combined to increase diagnostic accuracy. Although the models perform on simulation data-sets, no clinical relevance of the combination is observed in the applied real-data

Cyberbullying Experienced by University Students: A Study Protocol

Background: Bullying appeared for the first time in the scientific literature in 18971. Access to the Internet and associated forms of digital communication open new environments for bullying, called cyberbullying. According to APA Dictionary of Psychology bullying is “persistent threatening and aggressive physical behavior or verbal abuse directed toward other people, especially those who are younger, smaller, weaker, or in some other situation of relative disadvantage” while “Cyberbullying is verbally threatening or harassing behavior conducted through such electronic technology as cell phones, e-mail, and text messaging”2. Bullying is perceived differently by young people in different cultures and environments and could lead to suicide-related ideology and behavior3. The aim of the study encompasses a tripartite nature. In the primary instance, it investigates the overarching effects of cyberbullying on the psychological well-being of college students. As a secondary point, the dissimilarities pertaining to gender, age, socio-economic background, and educational affiliations are examined. Furthermore, it focuses on the extent to which the students exhibit diminished levels of concentration and reduced focus on their academic achievements. Methods: This population-based cross-sectional study will include respondents of both genders aged 18 or above, with diverse socioeconomic statuses, and enrolled in Romanian universities. The instruments that will be used consist of self-administered online surveys, comprising a sociodemographic questionnaire, also assessing the academic level, a cyberbullying questionnaire survey, simultaneously evaluating patterns of internet utilization, and the 21-item Depression Anxiety Stress Scale (DASS-21). Discussion: The study delves into the intricacies surrounding cyberbullying and its potential multifaceted repercussions within college students. Given the dynamic evolution of digital communication and its profound influence on interpersonal dynamics, the study's significance resides in the promise of the findings to shed light on the challenges posed by this contemporary manifestation of aggression