Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons^™

Clinical Utility Of An Fda-Authorized Artificial Intelligence Imaging Platform In Interstitial Lung Disease Diagnosis, Arjun Prakash Tambe, Ryan Boente, Gautam George, Fayez Kheir, Omid Tahamtani Omran, Kavitha Selvan

Division of Pulmonary, Allergy, and Critical Care Medicine Faculty Papers

Background/Objectives: The diagnosis of interstitial lung disease (ILD) is challenging and frequently delayed. Clinically accessible and minimally invasive diagnostic tools are needed to expedite the diagnosis of ILD while minimizing risk to patients. Fibresolve is an imaging artificial intelligence (AI) tool recently approved by the Food and Drug Administration (FDA) for use in ILD diagnosis and made available to clinicians. The objective of this study was to describe its utility in clinical practice. Methods: We conducted a prospective, observational study of patients across the United States (US) in whom Fibresolve was utilized during routine clinical practice between July 2024 and …

Advocateai: A Human-In-The-Loop Artificial Intelligence Platform To Improve Diagnostic Trajectories And Patient Empowerment In Chronic Pelvic Pain, Gayatri Bhanot, Ashley Kochans

InnovateHER Meeting 2026

Chronic pelvic pain (CPP) affects up to 27% of women globally^1, yet diagnosis takes 4 – 12 years on average² — a crisis driven by healthcare fragmentation, systemic gender bias, and a 62% rate of symptom dismissal by healthcare providers³. AdvocateAI is a human-in-the-loop AI platform designed to empower patients to accelerate their own diagnostic journey. By synthesizing fragmented medical records and patient-reported symptoms, the tool creates structured clinical summaries and personalized advocacy scripts. Here, we present our findings from discovery, including a landscape analysis of available CPP treatments, a prototype co-designed by patients, and custom …

Dementia Detection In Low-Resource Languages: Evaluating Translation-Assisted Transfer Learning For Multilingual Clinical Assessment, Kylar A. Deloach

Honors Theses

Alzheimer's disease (AD) is a growing global health concern, with millions of people affected worldwide and cases expected to rise significantly in the coming decades. Early detection is critical for patient treatment and care, and recent advances in natural language processing (NLP) have shown promise in identifying linguistic markers associated with AD. However, most existing work has focused on English, leaving speakers of other languages with limited access to such tools. This study investigates how effective AD detection models trained on English data are at transferring to Greek, a low-resource language with limited dementia-related speech data available. We propose a …

Artificial Intelligence In Radiology: A Comparative Study Of Accuracy And Efficiency Across X-Ray, Ct, And Nuclear Medicine, Courtney J. Mello, Sophia D. Galvan, Britney R. Williams, Tuyen T. Thai, Phuong P. Mor, Saleha Zafar

Research Methods Poster Session 2026

Artificial intelligence (AI) is increasingly used in radiology to improve diagnostic accuracy and workflow efficiency; however, there is limited standardized research comparing its performance across imaging modalities. This study aimed to evaluate the accuracy and efficiency of AI in X-ray, computed tomography (CT), and nuclear medicine. A systematic review was conducted following PRISMA guidelines, analyzing peer-reviewed studies published between 2015 and 2025. Data were extracted on key performance metrics, including sensitivity, specificity, interpretation time, and error rates, to enable cross-modality comparison. Results demonstrated that AI achieved high diagnostic accuracy in X-ray imaging, with average sensitivity and specificity near 91% and …

What Are The Current Risks For Patients Associated With Technology Related Perfusion Practices: A Scoping Review, Jessica Steel

Student Works

Background: Advancements in medical technology, particularly in perfusion practices such as ECMO and heart-lung bypass, have improved patient care but also introduced novel risks. Technological errors—ranging from hardware malfunctions to software failures and AI model inaccuracies—can directly threaten patient safety.

Objective: To identify current trends and risks associated with technology in perfusion practices and synthesize evidence on the frequency, nature, and outcomes of technology-related errors.

Results: Evidence indicates substantial deficiencies in medical devices, predictive models, and health IT systems. AI-based predictive models frequently failed to detect critical health deterioration, missing 66% of injuries in some in-hospital mortality simulations. Adverse events …

Artificial Intelligence-Enhanced Cardiac Point-Of-Care Ultrasound: A Prospective Single-Arm Study, Lior Fisher, Michael Fiman, Yuval Yarkoni, Ella Segal, Boris Fishman, Kobi Faierstein, Noa Rubin, Adiel Am-Shalom, Howard Amital, Qiong Zhao, Smadar Kort, Praveen Mehrotra, Ehud Schwammenthal, Robert Klempfner, Eyal Zimlichman, Ehud Raanani, Elad Maor

Division of Cardiology Faculty Papers

OBJECTIVE: To evaluate the clinical utility of combining artificial intelligence (AI) with handheld focused cardiac ultrasound (FoCUS) performed by noncardiologist physicians in clinical care settings.

PATIENTS AND METHODS: In this prospective, single-arm study conducted from July 1, 2022, through December 31, 2023 (ClinicalTrials.gov NCT05455541), 660 adult patients presenting to the emergency department or internal medicine wards were assessed with handheld ultrasound devices enhanced by AI algorithms. These algorithms provided automated analysis of ventricular function, valvular disease, pericardial effusion, and inferior vena cava size. Participating physicians received focused training and performed examinations either in response to clinical suspicion or as part …

Automated Abdominal Aortic Calcification And Trabecular Bone Score Independently Predict Incident Fracture During Routine Osteoporosis Screening, Abadi K. Gebre, Marc Sim, Syed Zulqarnain Gilani, Afsah Saleem, Cassandra Smith, Didier Hans, Siobhan Reid, Barret A. Monchka, Douglas Kimelman, Mohammad Jafari Jozani, John T. Schousboe, Joshua R. Lewis, William D. Leslie, Joshua R. Lewis

Research outputs 2022 to 2026

Abdominal aortic calcification (AAC), a marker of subclinical cardiovascular disease, has previously shown to be associated with low BMD and fracture. However, it remains unclear whether AAC is associated with trabecular bone score (TBS), a gray-level textural measure, or whether it predicts fracture risk independent of this measure. Here, we examined the cross-sectional association of AAC scored using a validated machine learning algorithm (ML-AAC24) with TBS, and their simultaneous associations with incident fractures in 7691 individuals (93.4% women) through the Manitoba BMD Registry (mean age 75.3 yr). The association between ML-AAC24 and TBS was tested using generalized linear regression. Cox …

Defining A Multi-Omic, Ai-Enabled Stool Screening Paradigm For Colorectal Cancer: A Consensus Framework For Clinical Translation, Arturo Loaiza-Bonilla, Yan Leyfman, Viviana Cortiana, Rhys Crawford, Shivani Modi

Einstein Health Papers

Colorectal cancer (CRC) develops through both conventional adenoma-carcinoma and serrated neoplasia pathways, yet noninvasive screening still under-detects the advanced precursor lesions that enable true cancer prevention. Stool-based screening reduces CRC mortality, but its preventive impact remains constrained by limited detection of advanced precancerous lesions (APLs), including advanced adenomas and sessile serrated lesions. Next-generation multitarget stool DNA assays (mt-sDNA; e.g., Cologuard Plus) have established high sensitivity for CRC and specificity approaching 94%, leaving improved APL detection as the principal opportunity for innovation. This review presents a consensus framework for a multi-omic stool screening paradigm that integrates host epigenetic markers (DNA methylation) …

An Ai Approach To Differentiating Lung Squamous Cell Carcinoma From Metastases Of Other Origins, Mark G Evans, Jennifer Ribeiro, Todd Maney, Anthony Helmstetter, Jennifer Johnson, Anthony Karnezis, Casey Bales, George Sledge, David Spetzler, Ari Vanderwalde, Matthew Oberley, Balazs Halmos, Hossein Borghaei, Farah Abdulla, David Bryant, Fred Hirsch, Hassan Ghani

Department of Medical Oncology Faculty Papers

IMPORTANCE: Distinguishing primary lung squamous cell carcinoma (SCC) from squamous metastases to the lung is a clinical challenge due to histopathologic similarities. Accurate diagnosis is essential to guide treatment decisions.

OBJECTIVE: To assess the utility of an artificial intelligence (AI) approach that includes evaluation of key orthogonal evidence in distinguishing primary lung SCCs from metastatic tumors of other tissue origins.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used GPSai, a tissue-of-origin AI model run automatically on each sample submitted for molecular profiling, to flag potential misdiagnoses among research-eligible cases submitted as lung SCC. Molecularly profiled cases within the Caris Life …

Exploring The Optimal Age For Total Knee Arthroplasty To Minimize Risk Of Adverse Outcomes: Machine Learning Analysis Of A Statewide Cohort, Chloe Heiting, Yiyuan Wu, Susan M. Goodman, Peter Sculco, Fei Wang, Said A. Ibrahim, Peter Cram, Rich Caruana, Bella Mehta

Department of Medicine Faculty Papers

BACKGROUND: Rates of total knee arthroplasty (TKA) in the United States have risen in patients of a wide age range. Although rates of postoperative TKA complications have decreased, they remain a significant concern. In this study, we aim to determine how the risk of adverse TKA outcomes changes dynamically with age and explore the optimal ages with the lowest risk for adverse outcomes.

METHODS: This retrospective cohort study included patients who underwent elective primary TKA from 2012 to 2018 in the Pennsylvania Health Care Cost Containment Council Database. We trained (70% train:30% test) an explainable boosting machine (EBM), a modern …

Evolving Strategies In Prostate Cancer: Emerging Approaches And Unmet Needs From The Bridging The Gaps In Prostate Cancer Expert Panel, Rana Mckay, Benjamin Maughan, Alicia Morgans, Neal Shore, Evan Yu, Ravi Madan, Jacob Berchuck, Bradley Carthon, Steven Finkelstein, Leonard Gomella, Michael Gorin, Andrew Hahn, Stacy Loeb, Vivek Narayan, Daniel Petrylak, Charles Ryan, Karine Tawagi, Phuoc Tran, Tanya Dorff

Department of Urology Faculty Papers

BACKGROUND: The expansion of treatment options for prostate cancer (PC) has improved disease-specific and overall survival outcomes but has also raised questions about the optimal level of treatment needed for patients based on their individual prognosis and accounting for potential toxicity, incorporating quality of life considerations.

METHODS: A panel of experts met to discuss current controversies in the care of patients with PC across the disease continuum. Multidisciplinary experts review advances and persistent uncertainties in biomarker-guided assessment, imaging, and systemic therapy for prostate cancer. The discussion outlines priority gaps in evidence that must be addressed to optimize individualized patient care. …

Use Of Electrocardiograms To Identify Coronary Artery Disease: Cross-Validation Of An Artificial Intelligence Model, Michael Leasure, Indu Poornima, Adam Butchy, Utkars Jain, Devin Vasoya, Michael Warnick, Brent Williams, John Rehder, Prahlad Menon, Veronica A. Covalesky, Gary S. Mintz

Department of Medicine Faculty Papers

BACKGROUND: The current gold standard for the diagnosis of coronary artery disease (CAD) is invasive angiography; however, it is an invasive procedure. Therefore, we developed an artificial intelligence model designed to predict significant CAD from a resting digital 12-lead electrocardiogram (ECG).

OBJECTIVES: This retrospective study assessed the model's ability to predict clinically significant CAD in a patient population presenting for coronary angiography.

METHODS: From 2019 to 2021, 16,476 patients had a resting 12-lead digital ECG recorded within 90 days prior to coronary angiography. The artificial intelligence model was developed using 10-fold cross-validation methodology. Clinically significant disease was defined as angiographic …

Advances And Challenges In Digitally Connected Point-Of-Care Biosensing, Abdellatif Ait Lahcen, Jegan Rajendran, Gymama Slaughter

Center for Bioelectronics Publications

Point-of-care (POC) biosensors are undergoing a paradigm shift from isolated diagnostic tools to digitally connected, intelligent platforms that enable continuous and decentralized healthcare delivery. This review critically examines recent advances in wearable, implantable, and portable biosensors, highlighting how integration with wireless communication, the Internet of Medical Things (IoMT), and artificial intelligence is transforming their functionality and clinical utility. Particular attention is given to innovations such as smartphone-enabled interfaces, cloud-based analytics, and machine learning-assisted analysis, which collectively enhance sensitivity, specificity, and user accessibility across diverse healthcare settings, from personalized home monitoring and bedside diagnostics to deployment in resource-limited regions. The review …

Ai-Assisted Surface-Enhanced Raman Spectroscopy For Cardiovascular Diagnostics: From Plasmonic Materials To Clinical Translation, Anju Joshi, Gymama Slaughter

Center for Bioelectronics Publications

Raman spectroscopy (SERS) has emerged as a powerful analytical technique, offering molecular fingerprint specificity and ultrasensitive detection of cardiac biomarkers. Recent advances in plasmonic nanostructures, surface functionalization strategies, and flexible sensing platforms have significantly improved the analytical performance of SERS-based biosensors. In parallel, the integration of artificial intelligence (AI) and machine learning has enabled robust interpretation of complex spectral datasets, facilitating automated biomarker classification and improved diagnostic accuracy in heterogeneous biological environments. Despite these advances, the field remains fragmented, with limited integration between nanomaterial design, biomarker selection, and data-driven analysis, and persistent challenges related to reproducibility, standardization, and clinical validation. …

Maxgrnet: A Multi-Axis Vision Transformer With Improved Generalization For Eye Disease Classification Using Explainable Ai With Insertion-Deletion Operations On Fundus Images, Md Mehedi Hasan Santo, Fuyad Hasan Bhoyan, Fuad Ibne Jashim Farhad, Fahmid Al Farid, Sovon Chakraborty, Md Humaion Kabir Mehedi, Jia Uddin, Hezerul Bin Abdul Karim

Computer Science Faculty Publications

Eye diseases, including diabetic retinopathy (DR), glaucoma, and cataracts, represent a major global health concern and can lead to severe visual impairment or blindness if not identified in a timely manner. This study proposes a novel eye disease classification framework based on a multi-axis vision transformer (MaxViT) applied to color fundus images with Explainable Artificial Intelligence (XAI) techniques to enhance model transparency. The proposed architecture integrates transformer-based attention mechanisms with Global Response Normalization (GRN)-based multi-layer perceptron (MLP) layers to capture complex spatial and contextual relationships within fundus images effectively. The model was evaluated on a publicly available eye disease classification …

Clinician-Led Development And Feasibility Of A Neural Network For Assessing 3d Dental Cavity Preparations Assisted By Conversational Ai, Mohammed El-Hakim, Haitham Khaled, Amr Fawzy, Robert Anthonappa

Research outputs 2022 to 2026

Introduction: Artificial intelligence is emerging in dental education, but its use in preclinical assessment remains limited. Large language models like ChatGPT^® V4.5 enable non-programmers to build AI models through real-time guidance, addressing the coding barrier. Aim: This study aims to empower clinician-led, low-cost, AI-driven assessment models in preclinical restorative dentistry and to evaluate the technical feasibility of using a neural network to score 3D cavity preparations. Methods: Twenty mandibular molars (tooth 46), each with two carious lesions, were prepared and scored by two expert examiners using a 20-point rubric. The teeth were scanned with a Medit i700^® and …

Followership Theory In The Age Of Artificial Intelligence: A Phenomenological Study Of How The Integration Of Ai Into An Ai-Human Hybrid Cancer Research Team Affects Followership Characteristics, John F. Carrera

PhD in Organizational Leadership

Artificial intelligence (AI) is rapidly becoming part of cancer research teams, helping with data analysis, pattern recognition, and decision support. Although AI changes how work is completed, little is known about how AI changes how followers adapt when AI becomes part of the team. This study examined how AI influences followership characteristics within AIhuman hybrid cancer research teams. A qualitative phenomenological research design was used to explore the lived experiences of nine cancer research professionals who interacted with AI as part of their routine work. Semi-structured interviews were conducted, transcribed, and analyzed through multiple rounds of qualitative coding to find …

Artificial Intelligence In The Management Of Leukemia, Stephanie Koo, Austin P. Runde, Melvin Speisman

School of Medicine

BACKGROUND: Recently, given the demonstrated ability of AI to accurately characterize complex pathologies, AI has been proposed to be of use in the diagnosis, treatment, and monitoring of leukemias given their genetic complexity and subtype heterogeneity, array of treatments, and need for relapse detection. AI has several potential applications in the management of leukemia. First, it can be used to detect leukemia; using AI to detect nuances in lab values can ensure these deadly cancers are never missed. Second, AI can be used to risk-stratify patients and personalize treatments; leukemias are among the most genetically complex cancers with well-characterized risk …

Intelligence Architectures And Machine Learning Applications In Contemporary Spine Care, Rahul Kumar, Conor Dougherty, Kyle Sporn, Akshay Khanna, Puja Ravi, Pranay Prabhakar, Nasif Zaman

SKMC Student Presentations and Publications

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from …

Combination Of Irreversible Electroporation And Clostridium Novyi-Nt Bacterial Therapy For Colorectal Liver Metastasis, Zigeng Zhang, Guangbo Yu, Qiaoming Hou, Farideh Amirrad, Sha Webster, Surya M. Nauli, Jianhua Yu, Vahid Yaghmai, Aydin Eresen, Zhuoli Zhang

Pharmacy Faculty Articles and Research

Colorectal liver metastasis (CRLM) poses a significant challenge in oncology due to its high incidence and poor prognosis in unresectable cases. Current treatments, including surgical resection, systemic chemotherapy, and liver-directed therapies, often fail to effectively target hypoxic tumor regions, which are inherently more resistant to these interventions. This review examines the potential of a novel therapeutic strategy combining irreversible electroporation (IRE) ablation and Clostridium novyi-nontoxic (C. novyi-NT) bacterial therapy. IRE is a non-thermal tumor ablation technique that uses high-voltage electric pulses to create permanent nanopores in cell membranes, leading to cell death while preserving surrounding structures, and …

A Scoping Review On The Integration Of Artificial Intelligence In Point-Of-Care Ultrasound: Current Clinical Applications, Junu Kim, Sandhya Maranna, Caterina Watson, Nayana Parange

Research outputs 2022 to 2026

Background: Artificial intelligence (AI) is used increasingly in point-of-care ultrasound (POCUS). However, the true role, utility, advantages, and limitations of AI tools in POCUS have been poorly understood. Aim: to conduct a scoping review on the current literature of AI in POCUS to identify (1) how AI is being applied in POCUS, and (2) how AI in POCUS could be utilized in clinical settings. Methods: The review followed the JBI scoping review methodology. A search strategy was conducted in Medline, Embase, Emcare, Scopus, Web of Science, Google Scholar, and AI POCUS manufacturer websites. Selection criteria, evidence screening, and selection were …

A New Approach: Evaluating The Effectiveness Of Artificial Intelligence Developments For Trauma Triage In The Emergency Department, William C. Anoka

2025 Spring Honors Capstone Projects - Archive

Triage in emergent situations occurs when patients are assigned to areas of the hospital with different levels of care according to the perceived risks determined by their signs and symptoms. The variability of current triage protocols substantially increases the proportion of lower acuity patients who die unexpectedly due to improper risk stratification. However, recent artificial intelligence (AI) developments have been applied to trauma triage situations to improve accuracy and patient outcomes. To further evaluate the effectiveness of this new tool, this study used various AI models to assess the presenting assessments of multiple situations and determine the appropriate level of …

Midrc Mrale Mastermind Grand Challenge: Ai To Predict Covid Severity On Chest Radiographs, Samuel G. Armato, Karen Drukker, Lubomir Hadjiiski, Carol C. Wu, Jayashree Kalpathy-Cramer, George Shih, Maryellen L. Giger, Natalie Baughan, Benjamin Bearce, Adam E. Flanders, Robyn L. Ball, Kyle J. Myers, Heather M. Whitney, The Midrc Grand Challenge Working Group

Department of Radiology Faculty Papers

PURPOSE: The Medical Imaging and Data Resource Center (MIDRC) mRALE Mastermind Grand Challenge fostered the development of artificial intelligence (AI) techniques for the automated assignment of mRALE (modified radiographic assessment of lung edema) scores to portable chest radiographs from patients known to have COVID-19.

APPROACH: The challenge utilized 2079 training cases obtained from the publicly available MIDRC data commons, with validation and test cases sampled from not-yet-public MIDRC cases that were inaccessible to challenge participants. The reference standard mRALE scores for the challenge cases were established by a pool of 22 radiologist annotators. Using the MedICI challenge platform, participants submitted …

Evaluating A Large Language Model’S Accuracy In Chest X-Ray Interpretation For Acute Thoracic Conditions, Adam M. Ostrovsky

SKMC Student Presentations and Publications

BACKGROUND: The rapid advancement of artificial intelligence (AI) has great ability to impact healthcare. Chest X-rays are essential for diagnosing acute thoracic conditions in the emergency department (ED), but interpretation delays due to radiologist availability can impact clinical decision-making. AI models, including deep learning algorithms, have been explored for diagnostic support, but the potential of large language models (LLMs) in emergency radiology remains largely unexamined.

METHODS: This study assessed ChatGPT's feasibility in interpreting chest X-rays for acute thoracic conditions commonly encountered in the ED. A subset of 1400 images from the NIH Chest X-ray dataset was analyzed, representing seven pathology …

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 …

Visual Artificial Intelligence In Healthcare: A Revolution In Making, Sanjay S. Rao, Punnya. V. Angadi

Indian Journal of Health Sciences and Biomedical Research KLEU

Visual artificial intelligence (AI) is a branch of computer science that teaches robots to understand images and visual information similarly to how humans do. According to the algorithm's development, visual AI allows robots to do more than just perceive; it also allows them to understand and sense the meaning behind images. Because of the enormous progress made in this area, computers are now able to recognize and interpret images more accurately than humans. From managing medical data to enhancing care delivery through AI-assisted diagnosis, visual AI has a broad impact on healthcare. By building devices and tools that can learn, …

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 …

Artificial Intelligence In Cancer-Related Malnutrition And Cachexia: A Transformative Tool In Clinical Nutrition, Salvatore Carbone

EVMS School of Health Professions Faculty Publications

[Introduction] Malnutrition and cachexia are common complications in cancer patients, and they negatively influence prognosis, treatment efficacy, and tolerability as well as quality of life [[1], [2], [3]]. Accurately identifying and effectively managing malnutrition and cachexia in this population remains a clinical challenge. Conventional validated screening tools may lack the sensitivity and specificity required for early detection and personalized intervention in diverse cancer types and treatment settings [4,5]. Over the last decade, the use of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) strategies, has shown promising results in clinical nutrition, with the potential to revolutionize nutritional …

Why Ai Monitoring Faces Resistance And What Healthcare Organizations Can Do About It: An Emotion-Based Perspective, Karl Werder, Lan Cao, Eun Hee Park, Balasubramaniam Ramesh

Information Technology & Decision Sciences Faculty Publications

Continuous monitoring of patients' health facilitated by artificial intelligence (AI) has enhanced the quality of health care, that is, the ability to access effective care. However, AI monitoring often encounters resistance to adoption by decision makers. Healthcare organizations frequently assume that the resistance stems from patients' rational evaluation of the technology's costs and benefits. Recent research challenges this assumption and suggests that the resistance to AI monitoring is influenced by the emotional experiences of patients and their surrogate decision makers. We develop a framework from an emotional perspective, provide important implications for healthcare organizations, and offer recommendations to help reduce …

Can Residents Cheat The Unknown Slide Sessions By Using The Large Language Models? Which Model Should They Use: Chat-Gpt, Claude, Or Gemini?​, Gul Emek Wymer, Susana Ferra

East Florida Division GME Research Day 2025

Introduction: In recent years, artificial intelligence tools, such as large language models (LLMs) have expanded the potential for diagnostic medicine, including histopathology. This study aims to evaluate the diagnostic ability and utility of the publicly available large language models in predicting the accurate diagnosis of the unknown cases by using the images of the hematoxylin-eosin stained slides taken by a mobile phone and compare their performance with the residents’ performance.

Method: The twenty cases, including a variety of entities, were collected from teaching sets of non-HCA patients and public available domains, which are used for unknown slide sessions for residents. …