Physical Sciences and Mathematics Commons^™

What References Are Chatgpt, Gemini, Copilot, And Perplexity Providing For Consumer Health Questions?, Scott Johnson, Catherine Johnson, Ivan Portillo

Library Presentations, Posters, and Audiovisual Materials

Background

With the growing popularity of generative artificial intelligence (AI) models such as ChatGPT, consumers may turn to these tools to easily seek health information. To our knowledge, no study has analyzed the references provided by multiple models for consumer health questions.

Objective

We aimed to analyze the references provided by ChatGPT, Gemini, Copilot, and Perplexity for consumer health questions in order to determine the most frequently appearing references.

Methods

AI generative models ChatGPT 4.0, Google Gemini, Microsoft Copilot, and Perplexity were each asked 30 consumer health questions and prompted to provide the corresponding references. The references were recorded.

The …

Bioavailability Of Lead In Terre Haute Soils, Terre Haute, Indiana, Zach Kemp

All-Inclusive List of Electronic Theses and Dissertations

Contamination of soils from heavy metal pollution has been a global concern for decades. Heavy metals are released into the soil from natural and anthropogenic releases. The largest producers of heavy metal pollution include mining, industrial production, and the combustion of fossil fuels. Vigo County formed in 1818, and Terre Haute was established in 1832. After the discovery of coal in Clay County in 1867, Terre Haute became the third largest coal producer and fifth largest iron manufacturer in the state during that time. Lead, in particular, is a heavy metal of concern because of the harmful effects to humans …

Mechanistic Investigation Of Ring-Opening Polymerization Of Polycaprolactone Using Tin(Ii) Catalysts: Ligand Effects And Biomedical Applications, Eva-Larue M. Barber

Honors Scholar Theses

This research explores the mechanistic aspects of ring-opening polymerization (ROP) of ε-caprolactone (CL) to produce polycaprolactone (PCL), a biodegradable polymer widely used in biomedical applications. The study investigates how light exposure and catalyst concentration influence polymerization efficiency, using tin(II) 2-ethylhexanoate [Sn(Oct)₂] as the catalyst in a non-polar toluene solvent at 90 °C. Reactions were conducted under either ambient light or black light bulb (BLB) illumination, with monomer-to-catalyst ratios of 1:1 and 200:1.

Proton nuclear magnetic resonance (¹H NMR) spectroscopy was used to analyze conversion efficiency by tracking the disappearance of monomer signals and appearance of characteristic PCL peaks. Results revealed …

Implementing And Evaluating An Ai-Powered Visual Decision Support System To Improve Antibiotic Usage Among Physicians With A Built-In Early Warning System, Akua Sekyiwaa Osei-Nkwantabisa

Theses and Dissertations

The widespread misuse and excessive prescription of antibiotics have played a pivotal role in the emergence and proliferation of antibiotic-resistant bacteria, posing a critical global public health crisis. Addressing this challenge necessitates innovative solutions that enhance antimicrobial stewardship. This study presents the development and implementation of a visual decision support system designed to monitor and optimize antibiotic usage among healthcare providers. The proposed system integrates advanced machine learning algorithms with real-time data analytics to provide a dynamic, evidence-based decision support tool. Specifically, a neural network model was developed after evaluating multiple machine learning approaches, including Random Forest, Logistic Regression and …

Explainable Ai (Xai) For A Machine Learning Heart Disease Prediction Model, Sai Abhishek Sanchula

Electronic Theses, Projects, and Dissertations

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, necessitating the development of accurate and interpretable machine learning (ML) models for early diagnosis and risk assessment (World Health Organization, 2021). While ML algorithms such as logistic regression, decision trees, support vector machines (SVM) (Cortes & Vapnik, 1995), and deep learning models (LeCun et al., 2015) have demonstrated high predictive accuracy, their adoption in clinical practice is hindered by their black-box nature (Rudin, 2019). Explainable AI (XAI) techniques, including SHapley Additive Explanations (SHAP) (Lundberg & Lee, 2017), Local Interpretable Model-agnostic Explanations (LIME) (Ribeiro et al., 2016), and feature importance analysis …

Exploring The Biasing Effects Of Gender On Personality Disorder Diagnoses Formulated By Artificial Intelligence, Zoe Colclough

Student Theses

Gender bias is prevalent in personality disorder assessments, and while artificial intelligence has been posited as a solution to improve diagnostic objectivity and accuracy, the potential for such technologies to propagate human gender bias in mental health contexts remains underexplored. This study investigated the influences of gender bias on the diagnostic performance of ChatGPT-4o for personality disorders using three factorial research designs, which involved experimentally manipulating patient gender in a combined sample of 360 vignettes and case studies. Vignettes were synthesized through a novel artificial intelligence-assisted methodology established for this research, and case studies were identified from the literature. Significant …

Automated Radiotherapy Treatment Planning For Breast Cancer: A Robust To Ol For Global Deployment, Hana Baroudi

Dissertations and Theses (Open Access)

Breast cancer incidence continues to rise worldwide, particularly in low- and middle-income countries, where limitations in resources already constrain access to timely care. Radiotherapy is a cornerstone of breast cancer management, proven to significantly lower both recurrence and mortality. However, a growing shortage of radiation staff worldwide threatens the prompt delivery of these treatments.This thesis proposes an automated solution to address the limited accessibility of radiotherapy planning in breast cancer management by developing an end-to-end automated treatment planning model and evaluating its performance and limitations across diverse patient populations.

An automated contouring model was trained using data from 104 whole-breast …

Development Of Lineal Energy Spectrum-Based Biological Effects Models For Protons, Joseph M. Decunha, Fada Guan, David Grosshans, Zhongxing Liao, Dragan Mirkovic, Oleg Vassiliev, Radhe Mohan

Dissertations and Theses (Open Access)

In this dissertation, methods are developed and described to allow for the rapid calculation of microdosimetric spectra (specifically, lineal energy) for protons. SuperTrack, a GPU-accelerated tool for calculation of microdosimetric spectra was developed and is capable of computing lineal energy spectra up to 5000x faster than using Geant4 directly. Proton lineal energy spectra generated by SuperTrack are indistinguishable from those generated by Geant4. With SuperTrack, large libraries of lineal energy spectra for monoenergetic protons spanning 0-300 MeV have been developed. The proton lineal energy spectra calculated by SuperTrack have been compared to experimental measurements made by a tissue equivalent proportional …

Biophysical Studies Of Nucleic Acid - Small Molecule Recognition, Andrea Conner

All Dissertations

The presented work is a collection of studies performed to examine small molecule recognition of nucleic acids. Recognition of nucleic acids was examined using two types of small molecules: aminoglycosides and Hoechst dyes. Chapter One focuses on established experimental techniques (i.e. absorbance, isothermal titration calorimetry, flow cytometry, cell microscopy, etc.) in small molecule recognition of nucleic acids. The emphasis of studies in the group has been on two focus areas. (i) Thermodynamic characterization of mixed A-form and B-form DNA or RNA conformation with aminoglycosides, particularly neomycin-class. (ii) Characterization of new Hoechst-morpholine (HT-MO) derivatives’ interactions with nucleic acids.

Focus area (i) …

The Efficacy Of Incorporating Artificial Intelligence (Ai) Chatbots In Brief Gratitude And Self-Affirmation Interventions: Evidence From Two Exploratory Experiments, Jing Wen Hung, Andree Hartanto, Adalia Y.H. Goh, Zoey K.Y. Eun, K. T. A. Sandeeshwara Kasturiratna, Zhi Xuan Lee, Nadyanna M. Majeed

Research Collection School of Social Sciences

Numerous studies have demonstrated that positive psychology interventions, including brief interventions, can significantly improve well-being outcomes. These findings are particularly important given that many of these interventions are brief and self-administered, making them both accessible and scalable for large populations. However, the efficacy of positive psychology interventions is often constrained by small effect sizes. In light of advancements in generative Artificial Intelligence (AI), this study explored whether integrating AI chatbots into positive psychology interventions could enhance their efficacy compared to traditional self-administered approaches. Study 1 examined the efficacy of a gratitude intervention delivered through Snapchat's My AI, while Study 2 …

New Bayesian Methods For Longitudinal Data Analysis With Complex Dependence Structures., Anish Mukherjee

Electronic Theses and Dissertations

Longitudinal data in real-world settings are frequently found to be heterogeneous and exhibit intricate spatio-temporal dependence structures. Analyzing such complex data to obtain reliable estimation while quantifying uncertainty necessitates using sophisticated Bayesian methodology. In this work, we present novel Bayesian methods developed to address these challenges. We often observe heterogeneity in longitudinal data, where the mean and variance for certain profiles meaningfully differs from the rest. Some profiles may also exhibit outliers at a limited number of measurements. Using a standard mixed effects model, which assumes homogeneity, can lead to overestimating the residual variance and inefficient estimation. In this work, …

Advancing Drug-Drug Interaction Prediction Using Multi-Modal Feature Integration With Graph Neural Networks, Ernest C. Chianumba

Theses, Dissertations and Culminating Projects

Pharmaceutical treatments are essential for managing medical conditions, but drug-drug interactions (DDIs) pose significant risks to patient safety and healthcare outcomes. This research integrates Knowledge Graphs and Graph Neural Networks to predict DDIs by exploring complex drug relationships. We construct a comprehensive knowledge graph using DrugBank data (1,000 drugs, 155,774 interactions) enriched with molecular features from PubChem. Our methodology introduces a novel multi-modal approach by integrating transformer-based embeddings (ChemBERTa, SPECTER, and SBERT) to create 1152-dimensional feature vectors that capture structural, biomedical literature, and semantic properties of drugs. Formulating DDI prediction as a link prediction task, we compare three Graph Neural …

Machine Learning And Protein Engineering Approaches To Understanding Kinesin-5 Activity, Jason Eden Sanchez

Open Access Theses & Dissertations

Cancer is a term describing a collection of diseases that result in uncontrolled cell growth. Cancer has manifold etiologies and underlying cancers are rouge biochemical pathways involving many different proteins. In the current work, two approaches are used to enhance knowledge of kinesin-5, a potential cancer target involved in cell division. Kinesin-5 promotes cell division by cross-linking and separating microtubules in dividing cells. The first approach uses machine learning (ML) to identify small molecule inhibitors for kinesin-5. Though decades of research have uncovered classes of small-molecules which inhibit kinesin-5 in vitro and in vivo, no candidates have reached phase III …

Oculomics: Current Concepts And Evidence, Zhuoting Zhu, Yueye Wang, Ziyi Qi, Wenyi Hu, Xiayin Zhang, Siegfried K Wagner, Yujie Wang, An Ran Ran, Joshua Ong, Ethan Waisberg, Mouayad Masalkhi, Alex Suh, Yih Chung Tham, Carol Y Cheung, Xiaohong Yang, Honghua Yu, Zongyuan Ge, Wei Wang, Bin Sheng, Yun Liu, Andrew G Lee, Alastair K Denniston, Peter Van Wijngaarden, Pearse A Keane, Ching-Yu Cheng, Mingguang He, Tien Yin Wong

Faculty, Staff and Student Publications

The eye provides novel insights into general health, as well as pathogenesis and development of systemic diseases. In the past decade, growing evidence has demonstrated that the eye's structure and function mirror multiple systemic health conditions, especially in cardiovascular diseases, neurodegenerative disorders, and kidney impairments. This has given rise to the field of oculomics-the application of ophthalmic biomarkers to understand mechanisms, detect and predict disease. The development of this field has been accelerated by three major advances: 1) the availability and widespread clinical adoption of high-resolution and non-invasive ophthalmic imaging ("hardware"); 2) the availability of large studies to interrogate associations …

Joint Modelling Of Longitudinal Egfr Trajectory And Time To Acute Kidney Injury In Lung Transplant Patients, Samiha Zakir

Theses and Dissertations

Progressive declines in estimated glomerular filtration rate (eGFR) often precede acute kidney injury (AKI), yet the relationship between eGFR trends and AKI risk remains unclear. This study investigates longitudinal eGFR changes and their association with AKI in 459 lung transplant patients followed for up to 7 years (n = 6419). We applied a piecewise linear mixed-effects model to evaluate eGFR trajectories and a Cox proportional hazards model to assess time to AKI. A joint model was used to explore the interplay between longitudinal and survival processes. Key covariates included gender, age at transplantation, antibody-mediated rejection (AMR), and pre-transplant eGFR. Males …

Opioid Epidemic In Maine: An Analysis Of Increasing Overdose-Related Deaths Following The Coronavirus, Aysel S. Hamlin

Thinking Matters Symposium

The rate of drug overdose resulting in death doubled in Maine following the COVID-19 pandemic from the onset of the COVID-19 pandemic in late 2019 through 2022. The correlation between increased isolation during the pandemic and overdose death rates sheds a concerning light on the insufficient resources for people struggling with Opioid Use Disorder (OUD) throughout Maine. The increasing trade and access to fentanyl following the pandemic accounted for the majority of drug-related deaths in Maine in 2021 and 2022. This study examines the need for long-term access to drug treatment in rural and urban Maine, both environments with varying …

Artificial Intelligence In Orthopedic Medical Education: A Comprehensive Review Of Emerging Technologies And Their Applications, Kyle Sporn, Rahul Kumar, Phani Paladugu, Tejas Sekhar, Swapna Vaja, Tamer Hage, Ethan Waisberg, Chirag Gowda, Ram Jagadeesan, Nasif Zaman, Alireza Tavakkoli

SKMC Student Presentations and Publications

Integrating artificial intelligence (AI) and mixed reality (MR) into orthopedic education has transformed learning. This review examines AI-powered platforms like Microsoft HoloLens, Apple Vision Pro, and HTC Vive Pro, which enhance anatomical visualization, surgical simulation, and clinical decision-making. These technologies improve the spatial understanding of musculoskeletal structures, refine procedural skills with haptic feedback, and personalize learning through AI-driven adaptive algorithms. Generative AI tools like ChatGPT further support knowledge retention and provide evidence-based insights on orthopedic topics. AI-enabled platforms and generative AI tools help address challenges in standardizing orthopedic education. However, we still face many barriers that relate to standardizing data, …

The Effects Of Climate Change On The Advancement Of West Nile Virus, Mackenzie R. Epperson

ATU Scholars Symposium

The West Nile virus (WNV) first emerged in the United States in the year 1999 within the state of New York and has become endemic throughout the country over time. This virus is contracted from an avian species acting as the reservoir host, by the vector species which are mosquitoes. From this point, the mosquitoes transmit the virus to humans and other mammals, which are the dead end hosts. The WNV often presents itself as flu-like symptoms, but in serious cases can cause severe arboviral neurological disease. Since this is an arbovirus, it is important to have the ability to …

Detecting Ideological Bias In Trump's Tweets: Trump's Peace Plan As A Case Study, Amer Qasem

This study aims to explore the extent of ideological bias in the discourse of elected U.S. President Donald Trump on social media platforms, specifically Twitter (now known as X), regarding his peace plan, commonly referred to as the "Deal of the Century." The study employs Critical Discourse Analysis (CDA) to examine all of Trump’s tweets related to the Deal of the Century, seeking to answer the following research questions: How did Trump portray the parties affected by the deal in his tweets? What rhetorical strategies did he employ to present the deal? The analysis spans the period from the announcement …

The Use Of Artificial Intelligence Applications By Media Students In Palestinian Universities And Their Achieved Gratifications “A Field Study”, Said Shaheen Allani, Ahmad Al Sallaq

The study aimed to identify the extent to which media students in Palestinian universities use artificial intelligence applications and the achieved gratifications from it. The study belongs to the descriptive studies approach utilizing questionnaire as a tool for collecting data, for an available sample of (165) individuals that were distributed in five Palestinian universities. The study reached to; (54.5%) of media students in Palestinian universities use artificial intelligence applications, they use it for several reasons, the most important phrase is “it helps me to produce good digital content,” with a relative weight of (47.4%), it also achieves a number of …

Media Frameworks In Addressing Environmental Issues On Al Jazeera Net Website: An Analytical Study, Sabah Al Harahsheh

This study aimed to recognize the media frameworks used to address environmental issues on (Al Jazeera Net) between 2009 and 2024. The study relied on the descriptive survey method, the theory of media frames, and the content analysis as a method and tool. The study population consisted of all articles, reports and interviews published on the (Al Jazeera Net) throughout the period specified by the study. A purposive sample was selected from the articles, reports and interviews interested in environmental issues only, which numbered eighteen journalistic works. The study identified the most important frameworks that presented the environmental issues during …

The Mediating Role Of Remote Communication On The Relationship Between Electronic Human Resource Management Practices And Organizational Performance In Iraqi Commercial Banks, Shuhub Basel Ahmad Taqa

This study aimed to measure the impact of remote communication as a mediating variable on the relationship between electronic human resources (e-HRM) and organizational performance in Iraqi commercial banks. The study adopted a descriptive-analytical approach, with the study population consisting of managers, their deputies, and department heads in Iraqi commercial banks, totaling 242 individuals distributed across 24 commercial banks by the end of 2023. A total of 234 questionnaires were analyzed, achieving a response rate of 96.7%. The study employed Smart PLS software for data analysis and result extraction. The study revealed several key findings, the most significant of which …

Analysis And Monitoring Of A Robotics Curriculum: Are Simnow Modules Valuable?, Jacob Applegarth, Ibrahim Baida, Anthony Iacco, Ngan Nguyen, Nathan Novotny

Posters

No abstract provided.

From The Ground Up: Community-Based Participatory Research Reclaiming The Science Of Lead, Juan Manuel Rubio, Bavisha Kaylan, Anthony Diaz, Patricia Flores, Maya Cheav, David C. Bañuelas, Ashley Green, Annika Hjelmstad, Ariane Jong-Levinger, Tim Schütz, Maya Carrasquillo, Alana M. W. Lebron, Jun Wu

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

For decades, the dominant approach to lead poisoning has been to focus on homes affected by lead paint and to treat children who are already suffering from lead poisoning. This individualizing approach developed in the context of the defunding and deregulation of government agencies in the 1980s. In recent years, however, community–academic partnerships have reframed lead as an environmental issue produced by the development of the lead industry in the twentieth century and connected to overlapping histories of exploitation, discrimination, and inaction. These community-based projects have contributed to shifting research agendas (by emphasizing historical analysis and the study of the …

The Attitudes And Perspectives Of Laboratory Professionals On The Use Of Machine Learning Combined With Maldi For Viral Identification: A Qualitative Study, Grace Johnson

Honors Projects

The use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with machine learning (ML) has been proposed by numerous studies as a novel approach for viral identification. However, the development and implementation of this instrumentation is still in its early stages, and laboratory professionals' perspectives on its feasibility, accuracy, implementation, and effect on current laboratory operating procedures remain underexplored.

This study aimed to investigate laboratory professionals’ attitudes and opinions regarding the use of MALDI-TOF-MS coupled with machine learning for viral identification, focusing on perceived benefits, barriers, and factors that would affect participants’ opinions on implementation.

A qualitative descriptive research …

The Dialogue Dilemma: The Role Of Patient-Clinician Communication For Low-Income People Who Smoke And Manage Multiple Conditions, Monique T. Cano, Michael R. Lindstrom, Ricardo F. Muñoz

School of Mathematical & Statistical Sciences Faculty Publications

Introduction: Adults from low-income backgrounds who smoke face significant health disparities related to tobacco use, often at disproportionately high rates. These individuals are more likely to endure multiple mental and physical (MP) health conditions, which can negatively influence their self-rated health (SRH). The quality and effectiveness of patient-clinician communication (PCC) can influence how patients perceive their own health. Understanding how PCC influences SRH among low-income adults who smoke and suffer from multiple MP conditions is essential for clinical care as multimorbidity is on the rise. This study examines how PCC may influence the health perceptions of low-income adults who smoke …

Autoradai: A Versatile Artificial Intelligence Framework Validated For Detecting Extracapsular Extension In Prostate Cancer, Pegah Khosravi, Shady Saikali, Abolfazl Alipour, Saber Mohammadi, Maxwell Boger, Dalanda M. Diallo, Christopher J. Smith, Marcio C. Moschovas, Iman Hajirasouliha, Andrew J. Hung, Srirama S. Venkataraman, Vipul Patel

Publications and Research

Preoperative identification of extracapsular extension (ECE) in prostate cancer (PCa) is crucial for effective treatment planning, as ECE presence significantly increases the risk of positive surgical margins and early biochemical recurrence following radical prostatectomy. AutoRadAI, an innovative artificial intelligence (AI) framework, was developed to address this clinical challenge while demonstrating broader potential for diverse medical imaging applications. The framework integrates T2-weighted MRI data with histopathology annotations, leveraging a dual convolutional neural network (multi-CNN) architecture. AutoRadAI comprises two key components: ProSliceFinder, which isolates prostate-relevant MRI slices, and ExCapNet, which evaluates ECE likelihood at the patient level. The system was trained and …

The Analysis Of Reference Standard Storage Failures Through The Use Of Ibuprofen, Hope Vonder Brink

Honors Projects

Reference standards are commonly used in drug chemistry. They are used to calibrate the instruments and serve as a known point of reference for analysts’ ability to compare unknowns. Drugs can degrade over time or through environmental factors, causing contaminants to be present. In the event of environmental degradation while in storage, such as the exposure to high heat, it would be beneficial to have a database of known breakdown products in common drug reference standards for analysts’ reference. Using ibuprofen as an exemplar drug, this study attempted to identify the thermal breakdown products of ibuprofen and introduce the proposed …

Evaluating The Causal Effect Of Receiving Transthoracic Echocardiography On 28-Day Mortality In Mimic-Iii Intensive Care Unit Patients With Sepsis, Monserrath Velez, Jack O'Connor, Nicholas Della Pesca, Rio Baliga

Research & Creative Achievement Day

❏ Sepsis (an infection associated with vital organ dysfunction) is an emergent

medical condition estimated to occur in about 30% of intensive care unit

(ICU) patients[1] and is responsible for 20% of all deaths worldwide[2]

❏ Transthoracic echocardiography (TTE; an imaging modality that uses

ultrasound technology to record heart structure and function) is widely used in

medical treatment [3]

❏ 28-day mortality (whether or not a patient dies within 28 days after receiving a

treatment) is a measure considered to closely approximate ICU mortality[4]

Objectives

❏ Assess and quantify the causal effect of receiving a TTE on …

A Hybrid Deep Learning-Based Approach For Visual Field Test Forecasting, Ashkan Abbasi, Sowjanya Gowrisankaran, Wei-Chun Lin, Xubo Song, Bhavna Josephine Antony, Gadi Wollstein, Joel Schuman, Hiroshi Ishikawa

Wills Eye Hospital Papers

OBJECTIVE: Longitudinal assessment of visual field (VF) testing is essential in glaucoma management. Conventional VF forecasting methods require numerous prior tests, while deep learning techniques have shown promising results with fewer tests. This study introduces a hybrid deep learning framework to enhance flexibility and accuracy in VF test forecasting.

DESIGN: A retrospective longitudinal study using deep learning-based VF forecasting models.

SUBJECTS AND CONTROLS: A total of 1750 subjects (healthy and glaucoma patients) with 19 437 Humphrey VF (24-2 Swedish Interactive Threshold Algorithm) tests collected from longitudinal glaucoma cohorts at the University of Pittsburgh and New York University.

METHODS: Three deep …