Computer Sciences Commons^™

Designing Accessible Ui/Ux For Epileptic Patients: A Scalable Solution For Music Therapy Delivery, Amethyst G.H. Mckenzie

Computer Science Senior Theses

How can we design an accessible, scalable UI/UX system tailored to the cognitive, visual, and motor impairments of epileptic patients, that ensures safe and effective interactions with music therapy applications? This research explores the intersection of accessibility, user-centred design, and digital health, using an iterative design process to develop and refine the SONATA app—a clinically deployable music therapy platform.

Through two prototype iterations, usability testing, and quantitative event logging, this study compares the effectiveness of structured versus flexible navigation in improving user experience. Key findings reveal that structured navigation reduces unintended detours, while progressive disclosure techniques enhance instructional clarity. Additionally, …

The Present And Future Of Ai: Ethical Issues And Research Opportunities, Ankita Srivastava, Marco Marabelli, Danielle Blanch-Hartigan, Jeffrey Moriarty, Evan Carey

Philosophy Faculty Publications

Healthcare is currently a fast-changing industry with AI and generative AI (GenAI) playing a prominent role in the transformation of clinical as well as managerial practices. Clinical practices involve AI to diagnose diseases and develop new drugs and compounds, while managerial practices concern AI-supporting processes such as billing patients and insurance companies, handling electronic medical records, and supporting remote connections with patients, increasingly using virtual and augmented reality. Yet, all these opportunities offered by AI come with challenges involving potential ethical issues, such as discrimination, bias, lack of accessibility, and privacy issues. In March 2024, we organized a panel with …

Bpen: Brain Posterior Evidential Network For Trustworthy Brain Imaging Analysis, Kai Ye, Haoteng Tang, Siyuan Dai, Igor Fortel, Paul M. Thompson, R. Scott Mackin, Alex Leow, Heng Huang, Liang Zhan

Computer Science Faculty Publications

The application of deep learning techniques to analyze brain functional magnetic resonance imaging (fMRI) data has led to significant advancements in identifying prospective biomarkers associated with various clinical phenotypes and neurological conditions. Despite these achievements, the aspect of prediction uncertainty has been relatively underexplored in brain fMRI data analysis. Accurate uncertainty estimation is essential for trustworthy learning, given the challenges associated with brain fMRI data acquisition and the potential diagnostic implications for patients. To address this gap, we introduce a novel posterior evidential network, named the Brain Posterior Evidential Network (BPEN), designed to capture both aleatoric and epistemic uncertainty in …

Artificial Intelligence In Surgical Coding: Evaluating Large Language Models For Current Procedural Terminology Accuracy In Hand Surgery, Emily Isch, Jamie Lee, D. Mitchell Self, Abhijeet Sambangi, Theodore E. Habarth-Morales, John R. Vaile, E. J. Caterson

Department of Surgery Faculty Papers

PURPOSE: The advent of large language models (LLMs) like ChatGPT has introduced notable advancements in various surgical disciplines. These developments have led to an increased interest in the use of LLMs for Current Procedural Terminology (CPT) coding in surgery. With CPT coding being a complex and time-consuming process, often exacerbated by the scarcity of professional coders, there is a pressing need for innovative solutions to enhance coding efficiency and accuracy.

METHODS: This observational study evaluated the effectiveness of five publicly available large language models-Perplexity.AI, Bard, BingAI, ChatGPT 3.5, and ChatGPT 4.0-in accurately identifying CPT codes for hand surgery procedures. A …

Preserving Privacy In Senior Care At Home Monitoring Systems, Sam Russel, Ryan Benton, Amy Campbell, Scott Sittig

Shelby Hall Graduate Research Forum Posters

Many seniors prefer to live at home which necessitates research into the application of technologies to provide a safer environment with less caregiver resources. However, the application of home health care (HHC) monitoring for seniors is still in an evolutionary stage. Present HHC systems are produced by private companies with general regulatory guidelines lacking specific care of the elderly. As such, each company that produces such a system claims to have better safety, privacy, and security that their competitors. A pressing issues is devising and applying a general framework for the application of technologies that delivers safety while preserving privacy. …

False Narratives, Real Consequences, Russell W. Cantrell, Matt Campbell

Shelby Hall Graduate Research Forum Posters

Social media is an increasingly significant tool in modern cyber warfare, capable of rapidly shaping public opinion. The swift dissemination of information complicates efforts to distinguish fact from fiction [1]. During public health crises, healthcare professionals use these platforms to share updates, yet their credible content must contend with false or deliberately misleading narratives [2]. This environment creates an opportunity for cyberattacks through social media influence campaigns [3]. While disinformation's role in political interference has been widely studied, its potential to destabilize healthcare remains largely unexplored. Prior research primarily focuses on how vaccine misinformation affects the general public [4]. This …

Patient Consent And The Right To Notice And Explanation Of Ai Systems Used In Health Care, Meghan E Hurley, Benjamin H Lang, Kristin Marie Kostick-Quenet, Jared N Smith, Jennifer Blumenthal-Barby

Center for Medical Ethics and Health Policy Staff Publications

Given the need for enforceable guardrails for artificial intelligence (AI) that protect the public and allow for innovation, the U.S. Government recently issued a Blueprint for an AI Bill of Rights which outlines five principles of safe AI design, use, and implementation. One in particular, the right to notice and explanation, requires accurately informing the public about the use of AI that impacts them in ways that are easy to understand. Yet, in the healthcare setting, it is unclear what goal the right to notice and explanation serves, and the moral importance of patient-level disclosure. We propose three normative functions …

A Review Of Racial Differences And Disparities In Ecg, Jianwei Zheng, Chizobam Ani, Islam Abudayyeh, Yunfan Zheng, Cyril Rakovski, Ehsan Yaghmaei, Omolola Ogunyemi

Mathematics, Physics, and Computer Science Faculty Articles and Research

The electrocardiogram (ECG) is a widely used, non-invasive tool for diagnosing a range of cardiovascular conditions, including arrhythmia and heart disease-related structural changes. Despite its critical role in clinical care, racial and ethnic differences in ECG readings are often underexplored or inadequately addressed in research. Variations in key ECG parameters, such as PR interval, QRS duration, QT interval, and T-wave morphology, have been noted across different racial groups. However, the limited research in this area has hindered the development of diagnostic criteria that account for these differences, potentially contributing to healthcare disparities, as ECG interpretation algorithms largely developed from major …

Bloom: Behavioral Learning And Outcome Observation In Microbes, Sean Sarwar Haque, Luke Compton Wharton, Ming Lin, Razvan Voicu

Symposium of Student Scholars

Understanding how pathogens respond to physical changes in their environment is crucial for developing effective treatments and preventative measures. Current research often relies on static models or experimental data that either fail to capture the dynamic interactions within cellular environments or are not generalizable to other types of pathogens. This project aims to address this gap by creating a comprehensive cell simulation that models pathogens and their response to chemical, physical, and physiological changes. The proposed solution is a simulation that integrates biological data and computational modeling to replicate the behavior of pathogens in real time as they are affected …

Clinician Experiences With Ambient Scribe Technology To Assist With Documentation Burden And Efficiency, Matthew J. Duggan, Julietta Gervase, Anna Schoenbaum, William Hanson, John T. Howell, Michael Sheinberg, Kevin B. Johnson

SKMC Student Presentations and Publications

IMPORTANCE: Timely evaluation of ambient scribing technology is warranted to assess whether this technology can lessen the burden of clinical documentation on clinicians.

OBJECTIVE: To investigate the association of ambient scribing technology with efficiency, quality, and perceived burden of clinical documentation in the outpatient setting.

DESIGN, SETTING, AND PARTICIPANTS: This prospective, single-group pre-post quality improvement study was conducted between April and June 2024 in the outpatient setting of an academic health system in Philadelphia, Pennsylvania. Participants included physicians, nurse practitioners, and physician assistants. Data were analyzed from July to August 2024.

EXPOSURE: Access to an artificial intelligence-driven ambient scribing tool …

Self Supervised Artificial Intelligence Predicts Poor Outcome From Primary Cutaneous Squamous Cell Carcinoma At Diagnosis, Nicolas Coudray, Michelle C. Juarez, Maressa C. Criscito, Adalberto Claudio Quiros, Reason Wilken, Stephanie R. Jackson Cullison, Mary L. Stevenson, Nicole A. Doudican, Ke Yuan, Jamie D. Aquino, Daniel M. Klufas, Jeffrey P. North, Siegrid S. Yu, Fadi Murad, Emily Ruiz, Chrysalyne D. Schmults, Cristian D. Cardona Machado, Javier Cañueto, Anirudh Choudhary, Alysia N. Hughes, Alyssa Stockard, Zachary Leibovit-Reiben, Aaron R. Mangold, Aristotelis Tsirigos, John A. Carucci

Department of Dermatology and Cutaneous Biology Faculty Papers

Primary cutaneous squamous cell carcinoma (cSCC) is responsible for ~10,000 deaths annually in the United States. Stratification of risk of poor outcome at initial biopsy would significantly impact clinical decision-making during the initial post operative period where intervention has been shown to be most effective. Using whole-slide images (WSI) from 163 patients from 3 institutions, we developed a self supervised deep-learning model to predict poor outcomes in cSCC patients from histopathological features at initial diagnosis, and validated it using WSI from 563 patients, collected from two other academic institutions. For disease-free survival prediction, the model attained a concordance index of …

Artificial Intelligence (Ai) In Pharmacy, Giang Nguyen, Elizabeth Sartschev, John Reyes, Allie Honigford, Marisa Petrunich, Kiley Devoll, Brianna Lu, Joshua Honaker, T'Bony M. Jewell

Pharmacy and Wellness Review

Artificial Intelligence (AI) has transformed the pharmaceutical field by enabling computer software systems to learn and perform human behavior. Specifically, AI has revolutionized chronic diabetes management through continuous glucose monitoring, showcasing its immense potential in healthcare. However, alongside its transformative impact, AI’s increasing role in healthcare has prompted concerns over privacy and its premature integration. Despite these challenges, AI offers limitless opportunities to improve medication management and treatment regimens, driving advancements across various domains. From improving CT imaging to enhancing adenoma detection in colonoscopies and facilitating medication adherence, AI’s impact on healthcare is profound. Furthermore, AI plays a pivotal role …

Comparing In-Person, Standard Telehealth, And Remote Musculoskeletal Examination With A Novel Augmented Reality Exercise Game System: Pilot Cross-Sectional Comparison Study, Richard Wu, Keerthana Chakka, Sara Belko, Ninad Khargonkar, Kevin Desai, Balakrishnan Prabhakaran, Thiru Annaswamy

SKMC Student Presentations and Publications

BACKGROUND: Current telemedicine technologies are not fully optimized for conducting physical examinations. The Virtual Remote Tele-Physical Examination (VIRTEPEX) system, a novel proprietary technology platform using a Microsoft Kinect-based augmented reality game system to track motion and estimate force, has the potential to assist with conducting asynchronous, remote musculoskeletal examinations.

OBJECTIVE: This pilot study evaluated the feasibility of the VIRTEPEX system as a supplement to telehealth musculoskeletal strength assessments.

METHODS: In this cross-sectional pilot study, 12 study participants with upper extremity pain and/or weakness underwent strength evaluations for four upper extremity movements using in-person, telehealth, VIRTEPEX, and composite (telehealth plus VIRTEPEX) …

Effectiveness Of Postoperative Cephalosporins In Reducing Urinary Tract Infections And Other Parameters Following Transurethral Resection Of The Prostate: A Systematic Review And Meta-Analysis, Wael Hafez, Feras Al-Obeidat, Asrar Rashid, Arun Kumar Venkatachalapathi, Amr Massaod, Ziad Albaha, Samy Kishk, Tesfalidet Emoshe, Samuel Tesfaye Tefera, Ismail A. Ibrahim, Mohammad Alkammar, Gowhar Rashid, Ahmed Fayed, Karim Soliman, Abdulqadir J. Nashwan, Alaaldeen Mohamed, Daniel Simancas-Racines, Ivan Cherrez-Ojeda

All Works

No abstract provided.

Vaxbot-Hpv: A Gpt-Based Chatbot For Answering Hpv Vaccine-Related Questions, Yiming Li, Jianfu Li, Manqi Li, Evan Yu, Danniel Rhee, Muhammad Amith, Lu Tang, Lara S Savas, Licong Cui, Cui Tao

Faculty, Staff and Student Publications

OBJECTIVE: Human Papillomavirus (HPV) vaccine is an effective measure to prevent and control the diseases caused by HPV. However, widespread misinformation and vaccine hesitancy remain significant barriers to its uptake. This study focuses on the development of VaxBot-HPV, a chatbot aimed at improving health literacy and promoting vaccination uptake by providing information and answering questions about the HPV vaccine.

METHODS: We constructed the knowledge base (KB) for VaxBot-HPV, which consists of 451 documents from biomedical literature and web sources on the HPV vaccine. We extracted 202 question-answer pairs from the KB and 39 questions generated by GPT-4 for training and …

A Statistical Framework For Multi-Trait Rare Variant Analysis In Large-Scale Whole-Genome Sequencing Studies, Xihao Li, Han Chen, Margaret Sunitha Selvaraj, Eric Van Buren, Hufeng Zhou, Yuxuan Wang, Ryan Sun, Zachary R Mccaw, Zhi Yu, Min-Zhi Jiang, Daniel Dicorpo, Sheila M Gaynor, Rounak Dey, Donna K Arnett, Emelia J Benjamin, Joshua C Bis, John Blangero, Eric Boerwinkle, Donald W Bowden, Jennifer A Brody, Brian E Cade, April P Carson, Jenna C Carlson, Nathalie Chami, Yii-Der Ida Chen, Joanne E Curran, Paul S De Vries, Myriam Fornage, Nora Franceschini, Barry I Freedman, Charles Gu, Nancy L Heard-Costa, Jiang He, Lifang Hou, Yi-Jen Hung, Marguerite R Irvin, Robert C Kaplan, Sharon L R Kardia, Tanika N Kelly, Iain Konigsberg, Charles Kooperberg, Brian G Kral, Changwei Li, Yun Li, Honghuang Lin, Ching-Ti Liu, Ruth J F Loos, Michael C Mahaney, Lisa W Martin, Rasika A Mathias, Braxton D Mitchell, May E Montasser, Alanna C Morrison, Take Naseri, Kari E North, Nicholette D Palmer, Patricia A Peyser, Bruce M Psaty, Susan Redline, Alexander P Reiner, Stephen S Rich, Colleen M Sitlani, Jennifer A Smith, Kent D Taylor, Hemant K Tiwari, Ramachandran S Vasan, Satupa'itea Viali, Zhe Wang, Jennifer Wessel, Lisa R Yanek, Bing Yu, Nhlbi Trans-Omics For Precision Medicine (Topmed) Consortium, Josée Dupuis, James B Meigs, Paul L Auer, Laura M Raffield, Alisa K Manning, Kenneth M Rice, Jerome I Rotter, Gina M Peloso, Pradeep Natarajan, Zilin Li, Zhonghua Liu, Xihong Lin

Faculty, Staff and Student Publications

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally scalable analytical pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple …

Deep Learning-Based Auto-Segmentation For Liver Yttrium-90 Selective Internal Radiation Therapy, Jun Li, Wookjin Choi, Rani Anne

Department of Radiation Oncology Faculty Papers

The aim was to evaluate a deep learning-based auto-segmentation method for liver delineation in Y-90 selective internal radiation therapy (SIRT). A deep learning (DL)-based liver segmentation model using the U-Net3D architecture was built. Auto-segmentation of the liver was tested in CT images of SIRT patients. DL auto-segmented liver contours were evaluated against physician manually-delineated contours. Dice similarity coefficient (DSC) and mean distance to agreement (MDA) were calculated. The DL-model-generated contours were compared with the contours generated using an Atlas-based method. Ratio of volume (RV, the ratio of DL-model auto-segmented liver volume to manually-delineated liver volume), and ratio of activity (RA, …

Feasibility And Acceptability Of The Mazi Umntanakho Digital Tool In South African Settings: A Qualitative Evaluation, Catherine E. Draper, Caylee J. Cook, Elizabeth A. Ankrah, Jesus A. Beltran, Franceli L. Cibrian, Kimberley D. Lakes, Hanna Mofid, Lucretia Williams, Gillian R. Hayes

Engineering Faculty Articles and Research

To address the need for interventions targeting social emotional development and mental health of young children in South Africa, the Mazi Umntanakho (‘know your child’) digital tool was co-designed, and piloted with caregivers and 3–5-year-old children involved in home visiting programmes promoting early childhood development. The aim of this study was to qualitatively evaluate the feasibility and acceptability of this tool in four urban and four rural low-income communities, from the perspective of home visitors and caregivers. Focus groups were conducted with home visitors (n = 117) and caregivers (n = 72). Issues relating to the feasibility of …

Artificial Intelligence For The Detection Of Acute Myeloid Leukemia From Microscopic Blood Images; A Systematic Review And Meta-Analysis, Feras Al-Obeidat, Wael Hafez, Asrar Rashid, Mahir Khalil Jallo, Munier Gador, Ivan Cherrez-Ojeda, Daniel (Centro De Investigación De Salud Pública Y Epidemiología Clínica Simancas-Racines, , Universidad Ute, Quito, Ecuador , Universidad Ute, Quito, Ecuador

All Works

Leukemia is the 11th most prevalent type of cancer worldwide, with acute myeloid leukemia (AML) being the most frequent malignant blood malignancy in adults. Microscopic blood tests are the most common methods for identifying leukemia subtypes. An automated optical image-processing system using artificial intelligence (AI) has recently been applied to facilitate clinical decision-making. To evaluate the performance of all AI-based approaches for the detection and diagnosis of acute myeloid leukemia (AML). Medical databases including PubMed, Web of Science, and Scopus were searched until December 2023. We used the “metafor” and “metagen” libraries in R to analyze the different models used …

Optimizing Electrode Configurations For Eeg Mild Cognitive Impairment Detection, Yi Jiang, Xin Zhang, Zhiwei Guo, Xiaobo Zhou, Jiayuan He, Ning Jiang

Faculty, Staff and Student Publications

The Optimal electrode configuration of Electroencephalograms (EEG) systems for mild cognitive impairment (MCI) detection and monitoring in non-clinical settings, i.e. number of electrodes and the positions of the electrodes, remains to be explored. In the current study, we explored the optimization of electrode configuration for MCI detection. We used a 32-channel EEG device to record the data of 21 MCI patients and 20 cognitively normal elderly (NC) undergoing working memory (WM) tasks. Based on the differential value (MCI group vs. NC group) from the Power Spectral Density (PSD) value of each electrode in θ and α frequency band during WM …

Multiparametric Mri Along With Machine Learning Predicts Prognosis And Treatment Response In Pediatric Low-Grade Glioma, Anahita Fathi Kazerooni, Adam Kraya, Komal Rathi, Meen Chul Kim, Arastoo Vossough, Nastaran Khalili, Ariana Familiar, Deep Gandhi, Neda Khalili, Varun Kesherwani, Debanjan Haldar, Hannah Anderson, Run Jin, Aria Mahtabfar, Sina Bagheri, Yiran Guo, Qi Li, Xiaoyan Huang, Yuankun Zhu, Alex Sickler, Matthew R Lueder, Saksham Phul, Mateusz Koptyra, Phillip Storm, Jeffrey Ware, Yuanquan Song, Christos Davatzikos, Jessica Foster, Sabine Mueller, Michael J Fisher, Adam Resnick, Ali Nabavizadeh

Department of Neurosurgery Faculty Papers

Pediatric low-grade gliomas (pLGGs) exhibit heterogeneous prognoses and variable responses to treatment, leading to tumor progression and adverse outcomes in cases where complete resection is unachievable. Early prediction of treatment responsiveness and suitability for immunotherapy has the potential to improve clinical management and outcomes. Here, we present a radiogenomic analysis of pLGGs, integrating MRI and RNA sequencing data. We identify three immunologically distinct clusters, with one group characterized by increased immune activity and poorer prognosis, indicating potential benefit from immunotherapies. We develop a radiomic signature that predicts these immune profiles with over 80% accuracy. Furthermore, our clinicoradiomic model predicts progression-free …

Artificial Intelligence In Health Care: Business Opportunities And Ethical Challenges, Ankita Srivastava,, Marco Marabelli, Jeffrey Moriarty

Ethics Publication

Artificial intelligence (AI), and in particular generative AI (GAI), are making incredible progress toward automation. The pervasive and invasive nature of these technologies are affecting every industry, and health care is no exception. This paper summarizes insights derived from a panel that the Hoffman Center for Business Ethics and the Center for Health and Business at Bentley University hosted in March 2024. The panel invited three qualified health care professionals: Evan Carey, Susan Persky and John Torous. The panelists are all active in multiple aspects of AI in health care but represent a focus on the key areas of policy …

Chalkboards To Chatbots: Helping Faculty Harness Ai For The Future Of Higher Education, Justin C. Grace

Regis University Student Publications (comprehensive collection)

Integrating artificial intelligence (AI) into nursing education presented significant opportunities yet posed challenges due to varied faculty readiness. This Doctor of Nursing Practice (DNP) quality improvement (QI) project evaluated an educational intervention aimed at enhancing nursing faculty's AI proficiency and confidence at Regis University’s Rueckert-Hartman College for Health Professions. Using a mixed-methods, pre- and post-intervention design, validated surveys assessed changes in faculty perceptions, knowledge, and skills related to AI. The intervention included a digital toolkit with nine instructional videos demonstrating practical AI applications using FreedAI’s large language model, ChatGPT, supported by voiceover narration and closed captioning. Data analysis involved descriptive …

The Impacts Of Artificial Intelligence In Radiology, Misty Farmer, Wendy Trzyna

Theses, Dissertations and Capstones

Introduction: There has been significant growth in the use of Artificial Intelligence (AI) in the healthcare industry, especially in Medical Imaging. Radiology has been the clear frontrunner in the adoption of AI in medicine, due in part to the massive amount of digital data available for use in Deep Learning (DL) AI integration has the potential to solve multiple challenges in radiology, address workload issues and transform the field.

Purpose of the Study: The purpose of the research was to evaluate the impact of implementing Artificial Intelligence in radiology to determine if these technologies have had an impact …

A Survey-Based Quantitative Analysis Of Stress Factors And Their Impacts Among Cybersecurity Professionals, Sunil Arora, John D. Hastings

Research & Publications

This study investigates the prevalence and underlying causes of work-related stress and burnout among cybersecurity professionals using a quantitative survey approach guided by the Job Demands-Resources model. Analysis of responses from 50 cybersecurity practitioners reveals an alarming reality: 44% report experiencing severe work-related stress and burnout, while an additional 28% are uncertain about their condition. The demanding nature of cybersecurity roles, unrealistic expectations, and unsupportive organizational cultures emerge as primary factors fueling this crisis. Notably, 66% of respondents perceive cybersecurity jobs as more stressful than other IT positions, with 84% facing additional challenges due to the pandemic and recent high-profile …

Artificial Intelligence In Health Care: Business Opportunities And Ethical Challenges, Ankita Srivastava, Marco Marabelli, Jeffrey Moriarty

Philosophy Faculty Publications

Artificial intelligence (AI), and in particular generative AI (GAI), are making incredible progress toward automation. The pervasive and invasive nature of these technologies are affecting every industry, and health care is no exception. This paper summarizes insights derived from a panel that the Hoffman Center for Business Ethics and the Center for Health and Business at Bentley University hosted in March 2024. The panel invited three qualified health care professionals: Evan Carey, Susan Persky and John Torous. The panelists are all active in multiple aspects of AI in health care but represent a focus on the key areas of policy …

Artificial Intelligence In Health Care: Business Opportunities And Ethical Challenges, Ankita Srivastava, Marco Marabelli, Jeffrey Moriarty

Computer Information Systems Faculty Publications

Artificial intelligence (AI), and in particular generative AI (GAI), are making incredible progress toward automation. The pervasive and invasive nature of these technologies are affecting every industry, and health care is no exception. This paper summarizes insights derived from a panel that the Hoffman Center for Business Ethics and the Center for Health and Business at Bentley University hosted in March 2024. The panel invited three qualified health care professionals: Evan Carey, Susan Persky and John Torous. The panelists are all active in multiple aspects of AI in health care but represent a focus on the key areas of policy …

Quantifying Multidimensional Effects Of Physicochemical Parameters On Pfas Adsorption Using A Hybrid Response Surface Methodology-Machine Learning Approach, Harsh V. Patel, Jazmin Green, John Park, Stephanie Luster-Teasley Pass, Renzun Zhao

Engineering Management & Systems Engineering Faculty Publications

Per- and polyfluoroalkyl substances (PFAS) contamination has posed a significant environmental and public health challenge due to their ubiquitous nature. Adsorption has emerged as a promising remediation technique, yet optimizing adsorption efficiency remains complex due to the diverse physicochemical properties of PFAS and the wide range of adsorbent materials. Traditional modeling approaches, such as response surface methodology (RSM), struggled to capture nonlinear interactions, while standalone machine learning (ML) models required extensive datasets. This study addressed these limitations by developing hybrid RSM-ML models to improve the prediction and optimization of PFAS adsorption. A comprehensive dataset was constructed using experimental adsorption data, …

The Future Of Ai Regulation In Drug Development: A Comparative Analysis, Gabriela Lenarczyk, Timo Minssen, Nicholson Price, Arti Rai

Faculty Scholarship

As artificial intelligence (AI) transforms drug development, regulatory frameworks are evolving to oversee its implementation, particularly at the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). This paper makes three contributions to understanding emerging regulatory approaches. First, we offer a comparative analysis of how these agencies have responded to AI-driven advances, incorporating new US executive orders and the European Union (EU)’s AI Act. Second, we propose a novel analytical framework to understand regulatory divergence: the FDA’s flexible, dialog-driven model contrasts with the EMA’s structured, risk-tiered approach, reflecting broader institutional and political-economic differences. While the former encourages …

Artificial Intelligence In Radiology, Olivia Sweeney

Theses, Dissertations and Capstones

Introduction: Artificial intelligence (AI) has increasingly transformed radiologic practice by improving diagnostic accuracy, streamlining workflows, and reducing interpretation errors. As AI integration has expanded across imaging modalities, questions have emerged regarding its effectiveness compared to traditional radiologist-only interpretation.

Purpose of Study: The purpose of this study has been to evaluate the impact of AI-assisted radiology on diagnostic accuracy, efficiency, and error reduction, while also assessing clinician perceptions of AI as a collaborative tool in imaging analysis.

Methodology: This qualitative study has used a systematic review of peer-reviewed literature published between 2015 and 2025, following PRISMA guidelines, combined with an interview …