Health Information Technology Commons^™

Artificial Intelligence Policy Development In A Hybrid, Accelerated Dpt Program: Planning For Academic Integrity, Jennifaye V. Brown, Keiba Shaw, Otis L. Owens

Internet Journal of Allied Health Sciences and Practice

Purpose: Artificial intelligence (AI), particularly generative forms like ChatGPT, has rapidly grown across higher education. Institutions are developing policies for responsible/ethical use to preserve academic integrity, while acknowledging AI’s role in healthcare to confirm diagnoses and create personalized plans based on social determinants. In physical therapy education, AI can serve a valuable purpose despite documented misuse by Doctor of Physical Therapy (DPT) students during admissions and curricular activities. This paper describes the development of an AI policy following identified misuse and demonstrated knowledge gaps from student orientation essays. Methods: An AI task force was convened. Policy development was guided by …

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 …

Agreement Of Chatgpt With Clinical Practice Guidelines For Knee Osteoarthritis Treatment, Alessandra N. Garcia, Brian Neville, Bradley Myers, Lori Leineke, Karlyn Green

Internet Journal of Allied Health Sciences and Practice

Purpose: This cross-sectional study evaluated whether Chat Generative Pre-Trained Transformer (ChatGPT) GPT-3.5 and GPT-4O provide treatment information consistent with high-quality clinical practice guidelines (CPGs) for knee osteoarthritis (OA). Methods: High-quality CPGs published in the past decade were identified via PubMed and PEDro, with the search updated on November 11, 2024. Guidelines were appraised using the AGREE II tool. GPT-3.5 and GPT-4O were queried with common treatment-related questions, and their responses were compared to CPG recommendations. Two independent reviewers conducted a thematic content analysis of GPT-3.5 and GPT-4O responses using a deductive–inductive codebook, iteratively refined through consensus, to identify major themes/subthemes, …

Mediapipe-Based Extraction Of Joint Rom And Position From Patient Videos For The Functional Movement Screen: An Exploratory Study, Joshua Paul Verdillo, Lily Ann Bautista

Philippine Journal of Physical Therapy

Introduction. The Functional Movement Screen (FMS) is widely used to identify movement deficiencies and potential injury risk. However, its reliance on visual inspection introduces subjectivity, requires specialized training, and necessitates face-to-face assessment. Advances in computer vision, offer opportunities to automate and better objectify functional movement assessment. This exploratory study aimed to evaluate the feasibility of using a MediaPipe-based system, Software-Optimized Movement Assessment (SOMA), to extract joint range of motion (ROM), joint position, and compensatory movement patterns from patient videos during FMS performance.

Methods. SOMA was developed using MediaPipe Pose and MediaPipe Hands libraries integrated with Python-based tools to analyze videos …

Embracing Artificial Intelligence And Digital Health In Cancer Care, Pavitra P. Krishnamani

Advances in Cancer Education and Quality Improvement

Digital health technologies like AI, telemedicine, and wearables are revolutionizing cancer care by enabling earlier detection, personalized treatment, and improved patient support and access.

The New Professional: What New And Associate Dentists Need To Know About Ai, Amrita Patel Dds

The Journal of the Michigan Dental Association

This article outlines the growing importance of artificial intelligence (AI) in dentistry, particularly for newer professionals. AI's applications span radiographic analysis (identifying pathologies, enhancing diagnostic precision), treatment planning (simulating outcomes, designing aligners), and streamlining administrative tasks (voice recognition for notes, optimized scheduling). The author emphasizes that AI should be viewed as an augmentative tool to human capabilities, not a replacement for clinical judgment, especially in complex cases. It also highlights the critical considerations of data privacy (HIPAA compliance) and the enduring value of the relationship-driven nature of dentistry. Ultimately, the most successful dentists will be those who combine emerging technologies …

Transforming Healthcare Virtually: A Comprehensive Exploration Of Artificial Intelligence In Telehealth Across Internal Medicine Subspecialties, Jassimran Singh, Aditi Agrawal

Internet Journal of Allied Health Sciences and Practice

This review provides a comprehensive exploration of the transformative role of artificial intelligence (AI) in revolutionizing telehealth within various subspecialties of internal medicine. The focus is on enhancing patient care, addressing healthcare challenges, and mitigating professional burnout through the real-time, data-driven insights offered by AI. The integration of AI in telehealth is examined across diverse medical fields such as cardiology, pulmonary medicine, gastroenterology, endocrinology, geriatrics, nephrology, neurology, and hematology-oncology. The research methodology employs a systematic literature review and analysis approach, utilizing academic databases such as PubMed, Scopus, and IEEE Xplore to identify relevant literature up to July 2023. Inclusion criteria …

Implementation Of Artificial Intelligence For Scribing In An Outpatient Mental Health Clinic, Elissa A. Moore

Student Scholarly Projects

Practice Problem: Traditional documentation practices place a significant burden on psychiatric mental health nurse practitioners (PMHNP) in outpatient mental health clinics (MHC), contributing to provider burnout and negatively impacting the patient-provider relationship. Preserving practicing PMHNPs is essential across the United States, but imperative in medically underserved areas where mental health services are limited.

PICOT: In PMHNPs (Population), does the use of AI scribing software (Intervention) compared to traditional documentation methods (Comparison) reduce documentation time and increase provider satisfaction (Outcome) over a ten-week period (Time)?

Evidence: This Doctor of Nursing Practice (DNP) scholarly project was informed by evidence obtained from 15 …

Sexual Health In The Era Of Artificial Intelligence: A Scoping Review Of The Literature, Elia Abou Chawareb, Brian H. Im, Sherry Lu, Muhammed A.M. Hammad, Tiffany R. Huang, Henry Chen, Faysal A. Yafi

Student Papers, Posters & Projects

INTRODUCTION: Artificial Intelligence (AI) has witnessed significant growth in the field of medicine, leveraging machine learning, artificial neuron networks, and large language models. These technologies are effective in disease diagnosis, education, and prevention, while raising ethical concerns and potential challenges. However, their utility in sexual medicine remains relatively unexplored.

OBJECTIVE: We aim to provide a comprehensive summary of the status of AI in the field of sexual medicine.

METHODS: A comprehensive search was conducted using MeSH keywords, including "artificial intelligence," "sexual medicine," "sexual health," and "machine learning." Two investigators screened articles for eligibility within the PubMed and MEDLINE databases, with …

Perceptions Of Artificial Intelligence In Healthcare: A Qualitative Study Among Physicians And Nurses In Florida, Aaron Miri

MUSC Theses and Dissertations

This investigation will leverage participant focus group interviews with 32 clinicians (16 nurses / 16 physicians) to study what, if anything, is inhibiting AI adoption across the hospital. Specific physicians will be sourced across the key service lines of primary care, oncology, cardiology, behavioral health, and emergency department medicine, as these tend to be patient volume driven and thus have the maximum amount of potential for positive impact leveraging AI. Nurses in these same departments will be assessed to analyze if there is a similarity or difference between the nursing and physician AI adoption barriers.

Navigating Barriers: Challenges And Strategies For Adopting Artificial Intelligence In Qualitative Research In Low-Income African Contexts, Kahabi Isangula

School of Nursing & Midwifery, East Africa

Introduction:

 AI is transforming qualitative research. It enhances efficiency, accuracy, and depth in studies. Technologies like machine learning (ML), natural language processing (NLP), and large language models (LLMs) simplify tasks like transcription, coding, and thematic analysis. However, in low-income African settings, there are barriers to AI adoption. These include ethical concerns, infrastructure limitations, financial constraints, and technical skill gaps. Issues around data privacy and the dehumanization of research also add challenges.

Methods: 

This paper explores the challenges and opportunities of AI in qualitative research in low-income African contexts. It uses a descriptive approach, reviewing literature and personal experiences from rural …

A Bibliometric Analysis Of Ai-Driven Healthcare Literature Containing Kos Keywords: Trends, Themes, And Gaps, Julaine Clunis, Eric Asare

STEMPS Faculty Publications

As artificial intelligence (AI) becomes increasingly embedded in healthcare applications, concerns have emerged around the trustworthiness, interpretability, and context-awareness of these systems. Knowledge Organization Systems (KOS) hold considerable potential to address these challenges by supporting semantic standardization, explainability, and domain alignment. This study presents a bibliometric analysis of scholarly publications referencing both AI and healthcare concepts to examine how KOS are positioned within this evolving discourse. The findings indicate that while early literature frequently and explicitly referenced KOS—such as ontologies, controlled vocabularies, and classification systems—their visibility has declined relative to newer paradigms such as machine learning and large language models. …

A Qualitative Analysis Of College Students' Interest In Mhealth Solutions, Leslie Hoglund, Craig M. Becker, Cara Tonn

Health Behavior, Policy & Management Faculty Publications

This study explores college students' perceptions of an AI-driven mHealth application designed to promote well-being. With rising mental health challenges in academic settings, students increasingly seek digital tools that provide holistic support for physical, mental, and financial health. Through focus groups, this qualitative study examines students' preferences for personalized health tracking, educational content, and flexible reminders within a private, supportive community. Key findings emphasize students' desire for a balanced, all-in-one app that integrates health and wellness tools without overwhelming them with notifications. Students also highlighted the importance of social media integration for outreach, though concerns were raised about potential stress …

Impact Of Chatgpt And Large Language Models On Radiology Education: Association Of Academic Radiology-Radiology Research Alliance Task Force White Paper, David H. Ballard, Alexander Antigua-Made, Emily Barre, Elizabeth Edney, Emile B. Gordon, Linda Kelahan, Taha Lodhi, Jonathan G. Martin, Melis Ozkan, Kevin Serdynski, Bradley Spieler, Daphne Zhu, Scott J. Adams

School of Medicine Faculty Publications

Generative artificial intelligence, including large language models (LLMs), holds immense potential to enhance healthcare, medical education, and health research. Recognizing the transformative opportunities and potential risks afforded by LLMs, the Association of Academic Radiology-Radiology Research Alliance convened a task force to explore the promise and pitfalls of using LLMs such as ChatGPT in radiology. This white paper explores the impact of LLMs on radiology education, highlighting their potential to enrich curriculum development, teaching and learning, and learner assessment. Despite these advantages, the implementation of LLMs presents challenges, including limits on accuracy and transparency, the risk of misinformation, data privacy issues, …

National Use Of Artificial Intelligence For Eye Screening In Singapore, Dinesh Visva Gunasekeran, Steven Miller, Wynne Hsu, Mong Li, Tym Hon Wong, Mun Tuck Lee, Ecosse Lamoureau, Daniel Shu Wei Ting, Gavin Siew Wei Tan, Tien-Yin Wong

Research Collection School Of Computing and Information Systems

Diabetes is a major health care challenge, affecting 10% of the global population. One third of patients with diabetes have an ocular complication known as diabetic retinopathy (DR). DR progression to manifestations such as vision-threatening diabetic retinopathy (VTDR) remains the leading cause of blindness in working-aged adults. Yearly DR screening is a universally recommended practice in primary care settings for patients with diabetes, but it is often difficult to implement due to a lack of staffing and screening capacity in primary care. This case study highlights our experience with developing a medical artificial intelligence (AI) software-as-a-medical-device (SaMD) solution for DR …

The Recent History And Near Future Of Digital Health In The Field Of Behavioral Medicine: An Update On Progress From 2019 To 2024, Danielle Arigo, Danielle E Jake-Schoffman, Sherry L Pagoto

Rowan-Virtua School of Osteopathic Medicine Departmental Research

The field of behavioral medicine has a long and successful history of leveraging digital health tools to promote health behavior change. Our 2019 summary of the history and future of digital health in behavioral medicine (Arigo in J Behav Med 8: 67-83, 2019) was one of the most highly cited articles in the Journal of Behavioral Medicine from 2010 to 2020; here, we provide an update on the opportunities and challenges we identified in 2019. We address the impact of the COVID-19 pandemic on behavioral medicine research and practice and highlight some of the digital health advances it prompted. We …

Review Of Data Bias In Healthcare Applications, Atharva Prakash Parate, Aditya Ajay Iyer, Kanav Gupta, Harsh Porwal, P. C. Kishoreraja, R. Sivakumar, Rahul Soangra

Physical Therapy Faculty Articles and Research

In the area of medical artificial intelligence (AI), data bias is a major difficulty that affects several phases of data collection, processing, and model building. The many forms of data bias that are common in AI in healthcare are thoroughly examined in this review study, encompassing biases related to socioeconomic status, race, and ethnicity as well as biases in machine learning models and datasets. We examine how data bias affects the provision of healthcare, emphasizing how it might worsen health inequalities and jeopardize the accuracy of AI-driven clinical tools. We address methods for reducing data bias in AI and focus …

Diabetes Technology Meeting 2023, Tiffany Tian, Rachel E Aaron, Ashley Y Dunova, Johan H Jendle, David Kerr, Eda Cengiz, Andjela Drincic, John C Pickup, Kong Y Chen, Naomi Schwartz, Douglas B Muchmore, Halis K Akturk, Carol J Levy, Signe Schmidt, Riccardo Bellazzi, Alan H B Wu, Elias K Spanakis, Bijan Najafi, James Geoffrey Chase, Jane Jeffrie Seley, David C Klonoff

Center on Aging Staff Publications

Diabetes Technology Society hosted its annual Diabetes Technology Meeting from November 1 to November 4, 2023. Meeting topics included digital health; metrics of glycemia; the integration of glucose and insulin data into the electronic health record; technologies for insulin pumps, blood glucose monitors, and continuous glucose monitors; diabetes drugs and analytes; skin physiology; regulation of diabetes devices and drugs; and data science, artificial intelligence, and machine learning. A live demonstration of a personalized carbohydrate dispenser for people with diabetes was presented.

Digital Scribes: A Possible Solution For Provider Burnout By Reducing Provider Workload, Shannon Storley

Theses and Graduate Projects

Background: Provider burnout is continuing to be a massive problem for our healthcare industry. One major contributor to provider burnout is burdensome administrative tasks associated with documentation of electronic medical records (EMR). This review aims to uncover the applications for artificially intelligent digital scribes as a solution to reduce EMR documentation burden. Purpose: Provider burnout has shown to increase the incidence of major mistakes and decreased patient safety grades. Digital scribes could be a solution in reducing provider burnout by reducing the administrative burden of EMR documentation. Methods: A comprehensive literature review was conducted using articles from PubMed using search …

The Future Of Nursing Leadership: Incorporating E-Learned Artificial Intelligence (Ai) Pathways With A Precautionary Focus On Patient-Centered-Care, Jamie Anne Marcus Dr., Bonnette Villalba Webb

FDLA Journal

Artificial Intelligence (AI) is a data-driven mathematical process that incorporates machine-based-logic, usually in the form of algorithms. Education, training, and competencies are now conducted through virtual reality, robotics, simulation, and technology learning-based-platforms by healthcare organizations. This represents a significant change in the future of nursing practice. The adaptability of technology-based-learning platforms can impact the quality and efficiency of learning for some of the workforce population. Nurses' perception of technology and AI-driven nursing practice may vary based on generational orientation and can be a potential barrier to learning, practicing, and adaptability of this framework. The forging of well-trained resilient nurse leaders …

Unveiling The Potential: The Role Of Ai-Enhanced Ecg In Cardiovascular Disease Detection, Alisha Vincent

Rowan-Virtua Research Day

Background: The Electrocardiogram (ECG) is a widely utilized, non-invasive, cost-effective cardiac test. Its integration with Artificial Intelligence (AI) has empowered it to become a potent screening tool and a predictor for various cardiovascular diseases, especially in asymptomatic individuals. Objective: This review investigates the utility of AI-powered ECG in early detection of cardiac conditions, focusing on conditions such as low ejection fraction (LEF), atrial fibrillation (AF), aortic valve stenosis (AVS), and cardiac amyloidosis (CA). Methods: A literature review spanning 2018 to 2024 was conducted, analyzing 10 articles - 3 on AF, 3 on AVS, 3 on LEF, and …

“The Role Of Artificial Intelligence In The Pharmaceutical Field: Enhancing Therapeutic Outcomes And Repurposing Through The Acceleration Of Drug Discovery”, Tonivie Valeriano

[Archive] Belmont University Research Symposium (BURS)

The development of new drugs and their repurposing have considerably benefited the field of pharmacy. It will not only affect the pharmaceutical sector but also its diverse facets of health will be significantly influenced. Yet, the development of innovative medical treatments necessitated a lengthy period of expectancy for human survival. Individual survival rates were decreasing over time before the development of the treatment. Humanity has a limited lifespan. Moreover, investments in new drugs often go unnoticed because of the prolonged and complex process of drug research and development (R&D). In the future of pharmacy, artificial intelligence will continue to have …

Public Acceptance Of Using Artificial Intelligence-Assisted Weight Management Apps In High-Income Southeast Asian Adults With Overweight And Obesity: A Cross-Sectional Study, Han Shi Jocelyn Chew, Palakorn Achananuparp, Palakorn Achananuparp, Nicholas W. S. Chew, Yip Han Chin, Yujia Gao, Bok Yan Jimmy So, Asim Shabbir, Ee-Peng Lim, Kee Yuan Ngiam

Research Collection School Of Computing and Information Systems

Introduction: With in increase in interest to incorporate artificial intelligence (AI) into weight management programs, we aimed to examine user perceptions of AI-based mobile apps for weight management in adults with overweight and obesity. Methods: 280 participants were recruited between May and November 2022. Participants completed a questionnaire on sociodemographic profiles, Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), and Self-Regulation of Eating Behavior Questionnaire. Structural equation modeling was performed using R. Model fit was tested using maximum-likelihood generalized unweighted least squares. Associations between influencing factors were analyzed using correlation and linear regression. Results: 271 participant responses were …

Infusing Machine Learning And Computational Linguistics Into Clinical Notes, Funke V. Alabi, Onyeka Omose, Omotomilola Jegede

Mathematics & Statistics Faculty Publications

Entering free-form text notes into Electronic Health Records (EHR) systems takes a lot of time from clinicians. A large portion of this paper work is viewed as a burden, which cuts into the amount of time doctors spend with patients and increases the risk of burnout. We will see how machine learning and computational linguistics can be infused in the processing of taking clinical notes. We are presenting a new language modeling task that predicts the content of notes conditioned on historical data from a patient's medical record, such as patient demographics, lab results, medications, and previous notes, with the …

Innovation Process And Industrial System Of Us Food And Drug Administration-Approved Software As A Medical Device: Review And Content Analysis, Jiakan Yu, Jiajie Zhang, Shintaro Sengoku

Faculty, Staff and Student Publications

BACKGROUND: There has been a surge in academic and business interest in software as a medical device (SaMD). SaMD enables medical professionals to streamline existing medical practices and make innovative medical processes such as digital therapeutics a reality. Furthermore, SaMD is a billion-dollar market. However, SaMD is not clearly understood as a technological change and emerging industry.

OBJECTIVE: This study aims to review the landscape of SaMD in response to increasing interest in SaMD within health systems and regulation. The objectives of the study are to (1) clarify the innovation process of SaMD, (2) identify the prevailing typology of such …

Artificial Intelligence Is Revolutionizing Controlled Substance Diversion Detection, Brian Cox, Alberto Coustasse, Craig Kimble

Management Faculty Research

In community and institutional health care sectors, artificial intelligence (AI) use is expanding. AI is being tapped broadly in operations, customer service, and scheduling, with major pharmacy chains such as Kroger, CVS, and Walgreens, already starting to implement AI applications in their pharmacies. So far, Kroger has begun to use AI for employee onboarding and training processes, CVS is applying AI in negotiations with suppliers, and Walgreens is using it to streamline vaccine scheduling. With these advances in major pharmacy chains, the next extensive application for AI has become clearer: diversion monitoring. Diversion occurs in health care settings when a …

Singapore's Hospital To Home Program: Raising Patient Engagement Through Ai, John Abisheganaden, Kheng Hock Lee, Lian Leng Low, Eugene Shum, Han Leong Goh, Christine Gian Lee Ang, Andy Wee An Ta, Steven M. Miller

Research Collection School Of Computing and Information Systems

Because of their complex care needs, many elderly patients are discharged from hospitals only to be readmitted for multiple stays within the following twelve months. John Abisheganaden and his fellow authors describe Singapore’s Hospital to Home program, a community care initiative fueled by artificial intelligence.

Non-Melanoma Skin Cancer Detection In The Age Of Advanced Technology: A Review, Haleigh Stafford, Jane Buell, Elizabeth Chiang, Uma Ramesh, Michael Migden, Priyadharsini Nagarajan, Moran Amit, Dan Yaniv

Faculty, Staff and Student Publications

Skin cancer is the most common cancer diagnosis in the United States, with approximately one in five Americans expected to be diagnosed within their lifetime. Non-melanoma skin cancer is the most prevalent type of skin cancer, and as cases rise globally, physicians need reliable tools for early detection. Artificial intelligence has gained substantial interest as a decision support tool in medicine, particularly in image analysis, where deep learning has proven to be an effective tool. Because specialties such as dermatology rely primarily on visual diagnoses, deep learning could have many diagnostic applications, including the diagnosis of skin cancer. Furthermore, with …

Lessons Learned From The Hospital To Home Community Care Program In Singapore And The Supporting Ai Multiple Readmissions Prediction Model, John Abisheganaden, Kheng Hock Lee, Lian Leng Low, Eugene Shum, Han Leong Goh, Christine Gia Lee Ang, Adny An Ta Wee, Steven M. Miller

Research Collection School Of Computing and Information Systems

In a prior practice and policy article published in Healthcare Science, we introduced the deployed application of an artificial intelligence (AI) model to predict longer-term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home (H2H) program that has been operating since 2017. In this follow on practice and policy article, we further elaborate on Singapore's H2H program and care model, and its supporting AI model for multiple readmission prediction, in the following ways: (1) by providing updates on the AI and supporting information systems, (2) by reporting on customer …

Tracing The Twenty-Year Evolution Of Developing Ai For Eye Screening In Singapore: A Master Chronology Of Sidrp, Selena+ And Eyris, Steven M. Miller

Research Collection School Of Computing and Information Systems

This working paper is entirely comprised of a timeline table that begins in 2002 and runs through mid-2023. Across these two decades, this timeline traces the evolutionary development of the following:

• The early Singapore R&D efforts to apply software-based image analysis algorithms and methods to analyse eye retina images for diabetic retinopathy and other eye diseases. This was based on a collaboration between the Singapore Eye Research Institute (SERI) and its parent organization, the Singapore National Eye Centre (SNEC), with faculty from the School of Computing at National University of Singapore.
• The establishment and operation of the Singapore Integrated Diabetic …