Computer Sciences Commons^™

Cmos-Based Rotational Spectroscopy: Massive Spectral Fingerprint Generation And Molecular Detection With Deep Learning, Yasamin Fozouni

Computer Science and Engineering Theses and Dissertations

Rotational Spectroscopy is a powerful spectral fingerprinting approach that can be used for identifying different gas molecules in a sample. Gas molecules are free to rotate, with inertia, in fixed states of quantized energy. In Rotational Spectroscopy, radiative beams are shown onto a sample to cause an energy-based transition between quantized rotational states. By sweeping the frequency of the radiative beams and monitoring the absorption with a sensor, one can profile the different rotational states, monitoring for energy based transitions. These transitions are dependent on unique properties of the molecules, thus presenting a unique molecular identification fingerprint (in the form …

Neural Network Architecture Search Enabled Wide-Deep Learning (Nas-Wd) Integrated Hyperspectral Imaging Understanding For Woody Breast In Poultry Processing, Chaitanya Kumar Reddy Pallerla

Graduate Theses and Dissertations

The development and implementation of a Wide & Deep (WD) learning model tailored for classification and regression tasks utilizing spectral data provides a robust solution to evaluate woody breast (WB) conditions in poultry fillets. This process begins with thorough data preprocessing, which includes loading spectral and classification datasets, imputing missing values with medians, and splitting the data into training and testing sets to ensure rigorous model evaluation. The WD model architecture integrates wide linear models and deep neural networks to harness the strengths of both approaches. The wide component excels at memorizing sparse feature interactions, while the deep component captures …

Addressing Ethical Issues In Healthcare Artificial Intelligence Using A Lifecycle-Informed Process, Benjamin X Collins, Jean-Christophe Bélisle-Pipon, Barbara J Evans, Kadija Ferryman, Xiaoqian Jiang, Camille Nebeker, Laurie Novak, Kirk Roberts, Martin Were, Zhijun Yin, Vardit Ravitsky, Joseph Coco, Rachele Hendricks-Sturrup, Ishan Williams, Ellen W Clayton, Bradley A Malin, Bridge2ai Ethics And Trustworthy Ai Working Group

Faculty, Staff and Student Publications

OBJECTIVES: Artificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation.

MATERIALS AND METHODS: We analyzed existing lifecycles from within the current literature for ethical issues of AI in healthcare to identify themes, which we relied upon to create a lifecycle …

Time Series Decomposition Of Land Surface Temperature For Long-Term Trend Forecasting And Impact On Nesting Sea Turtle Habitats In The Arabian Gulf, Sachi Perera, Rommel H. Maneja, Mohamed Allali, Cyril Rakovski, Erik Linstead, Daniele Struppa, Ali Qasem, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Improving land surface temperature (LST) modeling is vital for mitigating climate change effects on various ecosystems and marine habitats such as important sea turtle habitats. Over the past decade, extreme temperatures have likely significantly affected nesting sea turtle habitats in the Arabian Gulf, with predominantly female hatchlings creating an imbalance in the sex ratio. Such shifts have profound implications for these habitats’ long-term survival and conservation management. This study leverages statistical machine learning models to measure ongoing temporal variations in LST. We break down the LST time series into trend, seasonal, and noise components using classical decomposition methods like X11, …

Artificial Intelligence And Machine Learning In Cancer Pain: A Systematic Review, Vivian Salama, Brandon Godinich, Yimin Geng, Laia Humbert-Vidan, Laura Maule, Kareem A Wahid, Mohamed A Naser, Renjie He, Abdallah S R Mohamed, Clifton D Fuller, Amy C Moreno

Faculty, Staff and Student Publications

Background/objectives: Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer.

Methods: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines.

Results: Forty four studies from 2006 to 2023 were included. Nineteen studies used …

Neural Network Architecture Search Enabled Wide-Deep Learning (Nas-Wd) For Spatially Heterogenous Property Awared Chicken Woody Breast Classification And Hardness Regression, Chaitanya Pallerla, Yihong Feng, Casey M. Owens, Ramesh Bahadur Bist, Siavash Mahmoudi, Pouya Sohrabipour, Amirreza Davar, Dongyi Wang

Poultry Science Faculty Publications and Presentations

Due to intensive genetic selection for rapid growth rates and high broiler yields in recent years, the global poultry industry has faced a challenging problem in the form of woody breast (WB) conditions. This condition has caused significant economic losses as high as $200 million annually, and the root cause of WB has yet to be identified. Human palpation is the most common method of distinguishing a WB from others. However, this method is time-consuming and subjective. Hyperspectral imaging (HSI) combined with machine learning algorithms can evaluate the WB conditions of fillets in a non-invasive, objective, and high-throughput manner. In …

De-Identification Is Not Enough: A Comparison Between De-Identified And Synthetic Clinical Notes, Atiquer Rahman Sarkar, Yao-Shun Chuang, Noman Mohammed, Xiaoqian Jiang

Faculty, Staff and Student Publications

For sharing privacy-sensitive data, de-identification is commonly regarded as adequate for safeguarding privacy. Synthetic data is also being considered as a privacy-preserving alternative. Recent successes with numerical and tabular data generative models and the breakthroughs in large generative language models raise the question of whether synthetically generated clinical notes could be a viable alternative to real notes for research purposes. In this work, we demonstrated that (i) de-identification of real clinical notes does not protect records against a membership inference attack, (ii) proposed a novel approach to generate synthetic clinical notes using the current state-of-the-art large language models, (iii) evaluated …

Study On The Seasonal Endobacterial Profile Of Avicennia Officinalis, Focusing On Core Bacterial Consortia Within Mangrove Pneumatophores, Devi R R, Vinod Prabhakar

Karbala International Journal of Modern Science

Microhabitats are smaller pockets of ecosystems with unique conditions and they serve as major contributors of species richness. Mangrove plants offer a diverse array of such microhabitats (rhizosphere, phyllosphere, endosphereetc), and the inhabitant bacteria benefit the host by providing better salinity tolerance, faster and better nutrient mobilization, protection from phytopathogens and assistance in seed germination. Identification of such beneficial bacteria,their growth requirementsandmetabolism, willhave direct application in the field of agriculture and ecosystem restoration. Hence in this study, effort has been made to elucidate the seasonal numerical and compositional profile of pneumatophore bacterial consortia of Avicennia officinalis, from the mangrove …

Analyzing And Extending Machine Learning Frameworks On High Risk Domains, Chengbin Hu

USF Tampa Graduate Theses and Dissertations

Machine learning (ML) has become a transformative force in high-risk domains such as genomics and cybersecurity, where accurate predictions and robust defenses are essential. This dissertation advances ML frameworks in these areas by developing methods to enhance predictive power in health applications and assess vulnerabilities in machine learning systems.

In the genomics field, the work addresses challenges in Non-Invasive Prenatal Testing (NIPT) of monogenic disorders by proposing a deep learning model that reconstructs the fetal genome using maternal plasma cell-free DNA (cfDNA) and parental whole-genome sequencing (WGS) data. This model achieves high accuracy in single nucleotide variation (SNV) prediction, surpassing …

Scholar Perspectives On The Impact Of A Scientific Community Program For Neurodivergent Undergraduate Stem Scholars, Dylan Sullivan, Fernando Zavala, Derek Hidalgo, Jacob Stolle, Rebecca Matte, Christin B. Monroe

Journal of Science Education for Students with Disabilities

Despite the adaptive strengths and unique problem-solving skills demonstrated by neurodivergent (ND) individuals, they remain underrepresented in Science, Technology, Engineering and Mathematics (STEM) fields. High unemployment rates among individuals with disabilities emphasize the need for addressing barriers to entry and persistence in the workforce. This study introduces a program designed to enhance opportunities for neurodivergent STEM scholars with financial needs, supported by the National Science Foundation (NSF). The program involves: 1) a weekly cohort course to engage in professional development, 2) use of the Birkman Method® survey to help scholars identify and communicate strengths, fostering self-awareness and growth, and 3) …

Predicting And Monitoring Immune Checkpoint Inhibitor Therapy Using Artificial Intelligence In Pancreatic Cancer, Guangbo Yu, Zigeng Zhang, Aydin Eresen, Qiaoming Hou, Farideh Amirrad, Sha Webster, Surya M. Nauli, Vahid Yaghmai, Zhuoli Zhang

Pharmacy Faculty Articles and Research

Pancreatic cancer remains one of the most lethal cancers, primarily due to its late diagnosis and limited treatment options. This review examines the challenges and potential of using immunotherapy to treat pancreatic cancer, highlighting the role of artificial intelligence (AI) as a promising tool to enhance early detection and monitor the effectiveness of these therapies. By synthesizing recent advancements and identifying gaps in the current research, this review aims to provide a comprehensive overview of how AI and immunotherapy can be integrated to develop more personalized and effective treatment strategies. The insights from this review may guide future research efforts …

Comparison Of Efficient Deep Learning Architectures For Lactobacillus Species Identification, Dea Aisyah Rusmawati, Ishak Ariawan, Afrinal Firmanda

Karbala International Journal of Modern Science

Identifying microorganism species, such as Lactobacillus, is essential in ensuring the food products' quality and safety. Traditional laboratory practice requires expert knowledge and experience, but the method is expensive and time-consuming due to complex sample preparation. Faster, more accurate, and cheaper computational methods, such as transfer learning technology, are needed for the Lactobacillus species classification. The technique has been effective in a variety of image recognition contexts. Deep learning architecture can also be applied as an innovative strategy for digital image-based identification. Therefore, this research aims to compare several deep-learning architectures in classifying bacterial strains of Lactobacillus. The four architectures …

Classification Manner Utilizing Electroencephalography Signals To Investigate Waveforms, Wessam Al-Salman, Ali Basim Al-Khafaji, Mishall Al-Zubaidie

Karbala International Journal of Modern Science

Waveform detection has been an area of continuous investigation for many years. One important waveform in sleep stage 2 is the k-complex. Numerous researchers have created various strategies for auto-k-complex detection; some of these strategies state that the automated detection techniques are adequate. Because of its analytically relevant resolution, the Electroencephalogram (EEG) is a commonly utilized technique to analyze the k-complexes in order to understand the nervous system activity of the brain. Several researchers have classified waveforms using EEGs in a variety of ways. It appears that the majority of the waveform detection had limitations. The …

Bacterial Cellulose Production From Fermented Fruits And Vegetables Byproducts: A Comprehensive Study On Chemical And Morphological Properties, Yati Maryati, Hakiki Melanie, Windri Handayani, Yasman Yasman

Karbala International Journal of Modern Science

This study aimed to produce α-cellulose from Bacterial Cellulose SCOBY (BCS) using alternative substrates from fermented fruit and vegetable byproducts: katuk leaves (KT), kale leaves (KL), guava (JB), dragon fruit (NG), and banana (PS). BCS production involved juice extraction, SCOBY inoculation, and sucrose addition, followed by 21 days of fermentation. Initially, the NG medium had the highest concentration of Total Reducing Sugars (TRS), but all media showed a decline as sugars were consumed. Fermentation reduced pH and increased total polyphenols, with KL and JB showing the highest rise (0.13-0.15 mg GAE/mL). Flavonoid levels varied, decreasing in KL and PS but …

Metabolite Profiling Of Potential Fraction From Ethyl Acetate Extract Of Ziziphus Mauritiana Leaves By Lc-Ms/Ms Analysis, Nurhayati Bialangi, Weny Ja Musa, Boima Situmeang

Karbala International Journal of Modern Science

In Indonesia, the treatment with leaves as a traditional medicine is still firmly integrated into the community to overcome various health problems experienced, one of which is the treatment with Bidara leaves (Ziziphus mauritiana). Ziziphus mauritiana belongs to family of Rhamnaceae, and it is generally considered a potential source of antioxidant and cholesterol lowering. This study aimed to examine phytochemical characterization, antioxidant potential, and cholesterol-lowering effects of ethyl acetate fraction derived from Ziziphus mauritiana. Extraction was accomplished using ethyl acetate, and the resultant extract was fractionated through chromatography with a blend of n-hexane and ethyl acetate as …

Toward A Responsible Future: Recommendations For Ai-Enabled Clinical Decision Support, Steven Labkoff, Bilikis Oladimeji, Joseph Kannry, Anthony Solomonides, Russell Leftwich, Eileen Koski, Amanda L Joseph, Monica Lopez-Gonzalez, Lee A Fleisher, Kimberly Nolen, Sayon Dutta, Deborah R Levy, Amy Price, Paul J Barr, Jonathan D Hron, Baihan Lin, Gyana Srivastava, Nuria Pastor, Unai Sanchez Luque, Tien Thi Thuy Bui, Reva Singh, Tayler Williams, Mark G Weiner, Tristan Naumann, Dean F Sittig, Gretchen Purcell Jackson, Yuri Quintana

Faculty, Staff and Student Publications

BACKGROUND: Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging.

OBJECTIVES: This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients.

MATERIALS AND METHODS: In May …

Photoluminescence Intensity Enhancement And Stability In Cdte/Sio2 Quantum Dots Through Water Molecule Adsorption And Trap Passivation, Daniil S. Daibagya, Ivan A. Zakharchuk, Sergei A. Ambrozevich, Mikhail S. Smirnov, Anna V. Osadchenko, Oleg V. Ovchinnikov, Alexandr S. Selyukov

Karbala International Journal of Modern Science

The study of the luminescence photostability for colloidal nanocrystals is an important task since the understanding of the corresponding physical processes advances new electronic devices based on semiconductor nanoparticles as well as other important applications such as biomarkers. In this paper, we provide the first study and comprehensive analysis of the photostability of the luminescent properties for colloidal CdTe/SiO₂ core/shell quantum dots prepared by an aqueous-based method. The quantum dots were exposed to continuous laser radiation during two time intervals with prolonged break in between. The photoluminescence intensity of the quantum dots increased over time under continuous laser irradiation. …

Removal Of Selenium Ions From Contaminated Aqueous Solutions By Adsorption Using Lemon Peels As A Non-Conventional Medium, Alanood A. Alsarayreh, Suha Anwer Ibrahim, Salem Jawad Alhamd, Thekra Atta Ibrahim, Mohammed Nsaif Abbas

Karbala International Journal of Modern Science

The disposal of heavy metals from various activities has become a pervasive issue. The investigation of sustain-able, low-cost adsorbents for the remediation of these hazardous pollutants has been widely disregarded. This research emphasizes the recovery of selenium ions from contaminated water using lemon peels as a cost-effective adsorbent. The adsorption was explored in a batch unit under various operational parameters, including initial selenium concentration (1-90 ppm), pH (1-11), agitation speed (100-500 rpm), adsorbent dose (0.4-5.5 g), contact time (5-180 minutes), and temperature (25-55 °C). The BET surface area of the lemon peels was found to be 27.86 m²/g before adsorption …

Interpreting The Biological Effects Of Protons As A Function Of Physical Quantity: Linear Energy Transfer Or Microdosimetric Lineal Energy Spectrum?, Fada Guan, Lawrence Bronk, Matthew Kerr, Yuting Li, Leslie A Braby, Mary Sobieski, Xiaochun Wang, Xiaodong Zhang, Clifford Stephan, David R Grosshans, Radhe Mohan

Faculty, Staff and Student Publications

The choice of appropriate physical quantities to characterize the biological effects of ionizing radiation has evolved over time coupled with advances in scientific understanding. The basic hypothesis in radiation dosimetry is that the energy deposited by ionizing radiation initiates all the consequences of exposure in a biological sample (e.g., DNA damage, reproductive cell death). Physical quantities defined to characterize energy deposition have included dose, a measure of the mean energy imparted per unit mass of the target, and linear energy transfer (LET), a measure of the mean energy deposition per unit distance that charged particles traverse in a medium. The …

Gas Chromatography-Mass Spectrometry (Gc-Ms), Computational Analysis, And In Vitro Effect Of Essential Oils From Two Aromatic Plants, Bubonium Graveolens And Launaea Arborescens Growing In Southwest Algeria Against Potato Cyst Nematodes, Souad Ziane, Chaouki Selles, Khaldun M. Al Azzam, Bounoua Nadia, Belal O. Al-Najjar, Ali Al-Samydai, Obada A. Sibai, El-Sayed Negim

Karbala International Journal of Modern Science

The study tested the nematicidal effects of essential oils from Bubonium graveolens and Launaea arborescens on the potato cyst nematode Globodera rostochiens. The chemical composition of the essential oils was analyzed using GC-MS. To determine the concentration that killed 50% of the nematode population (LC50), five concentrations of the essential oils were applied to the tested organisms. The effects of essential oils on the hatching of cyst nematode (Globodera rostochiensis sp.) eggs in vitro demonstrated a wide variety of effects ranging from no impact to mild, moderate, and strong effects, which increased dramatically with exposure duration and concentration. All the …

Broken Su(3) Description Of Energy Levels And Decay Properties In Gadolinium Isotopes (A=156-160), Fahmi Sh. Radhi, Amir Abdul Ameer Mohammed Ali Dr., Ali H. Al-Musawi

Karbala International Journal of Modern Science

This study presents an in-depth examination of the energy levels and decay properties of Gadolinium (Gd) isotopes with mass numbers (A=156-160), utilizing the Interacting Boson Model-1 (IBM-1) within a broken SU(3) symmetry framework. Through this approach, we systematically calculated and analyzed the energy spectra, B(E2) transition probabilities, quadrupole moments, and potential energy surface (PES) which provided valuable insights into the shape and collective behavior of nuclei, as well as the decay properties of the selected Gd isotopes. The broken SU(3) symmetries provide a good description to the isotopes under study. This comprehensive analysis enhances the understanding of the nuclear structure …

Skin Microbiome: Current Target For Cosmeceuticals, Priyanka Kakkar, Neeraj Wadhwa

Karbala International Journal of Modern Science

Skin acts as a barrier to the external environment and perform various functions like maintaining internal homeostasis, sensations to touch based stimuli, vitamin D production and defence against foreign pathogens, prevent dehydration. Skin has its own diverse microbiota like bacteria, virus, fungi that is collectively called as skin microbiome. Skin microbiome balance is disturbed (condition called dysbiosis) by both internal and external factors which lead to skin problems like acne, psoriasis, dandruff. It is important to maintain the healthy skin ecosystem. Cosmeceuticals i.e., combination of cosmetic and pharmaceuticals, is a recent trend in the skin care industry where we add …

Road Signs Detection Using Ssd Mobilenetv2, Zahraa Salah Dhaif, Nidhal K. El Abbadi

Karbala International Journal of Modern Science

One of the most critical challenges for self-driving vehicles is accurately identifying traffic signs, which are essential for self-navigation and decision-making. Systems for the detection and recognition of road signs play a crucial role in this process by providing vital information for the vehicle's decision-making. This study proposes an approach for road sign identification and recognition utilising the TensorFlow Object Detection API and the SSD MobileNet V2 FPN Lite model.

In this proposal, we combine the efficiency and accuracy of SSD with the lightweight architecture of MobileNet to achieve excellent performance in object detection benchmarks while maintaining a small model …

Intelligrader: A Framework For Automatic Short Answer Grading, Inconsistency Check And Feedback In Educational Context - Conception, Implementation And Evaluation, Paradesi Sree Lakshmi, Jay B. Simha, Rajeev Ranjan

Karbala International Journal of Modern Science

Automatic Short Answer Grading (ASAG), an escalating realm in natural language understanding, constitutes a focal point of research within the broader field of learning analytics. Over time, many ASAG solutions have been proposed to address the difficulties in teaching. However, no work addressed three crucial aspects of evaluation together, i.e., i) automatic evaluation of brief subjective/descriptive answers written in English, ii) identifying the evaluation inconsistency, and iii) provision of providing feedback about inconsistent evaluation to the evaluator. The current work proposes IntelliGrader, a comprehensive ASAG system that addresses the above-mentioned issues. Automated grading is accomplished through a model answer-based approach. …

Isolation And Characterization Of Fermenting Bacterial Isolates From Vinegar Industry Waste In Local Markets Of Wasit Province, Tayseer Shamran Al-Deresawi

Karbala International Journal of Modern Science

The use of waste in the vinegar industry is an important practice of the food industry worldwide; however, this critical practice is neglected in many parts of Iraq. According to this, the current study was conducted to isolate and characterize fermenting bacterial organisms from this waste in Wasit Province, Iraq. Samples of vinegar industry by-products were collected from local businesses. These samples were prepared by using Hestrin-Schramm (HS) medium. Two methods, direct and indirect inoculations, were followed. The purified growth was examined using morphological and biochemical tests. Moreover, PCR was employed to confirm the identity of each bacterial isolate. The …

Ai And Future-Making: Design, Biases, And Human-Plant Interactions, Maliheh Ghajargar

Art Faculty Articles and Research

Design researchers and practitioners are turning to generative AI (genAI) to support activities such as ideation and concept development in pursuit of preferred futures. At the same time, genAI is known to have biases, which prompts questions about how these biases might adversely affect design practices. In the domain of sustainable HCI, with its recent trends in human-nature interactions and more-than-human design, the question can be further refined into whether and how genAI biases might perpetuate anthropocentric biases that these practices are increasingly seeking to confront. In the present research, we conducted three workshops, focusing on genAI for human-plant interactions; …

"The Words We Do Not Yet Have." A Creative Inquiry Into Human-Plant Relationships, Maliheh Ghajargar

Art Faculty Articles and Research

Climate change, loss of plant biodiversity, and ocean pollution signal the drastic changes in our ecology that call us to attend to the needs of more than human forms of life on Earth. Sustainable design and HCI research are responding to this call by offering methods and approaches to design more sustainable products and systems and recently, more than human design is building momentum. This agenda seeks to reform traditional design processes by decentering the creative agency of the dominant socio-economical group of humans and foregrounding those of diverse Others. In this paper, I focus on plants as a nonhuman …

Weed Seed Wizard Case Study - Don't Stop Harvest Weed Management Because It’S A Dry Year, Department Of Primary Industries And Regional Development, Western Australia

Biosecurity research reports

The Weed Seed Wizard is a national collaborative project that uses paddock management information to predict weed emergence and crop losses now and in the future.

The Weed Seed Wizard is a computer simulation tool that:

• applies to all Australian grain growing areas

• helps growers understand and manage weed seedbanks on their farms

• uses farm management records to simulate how different crop rotations, weed control techniques, irrigation, grazing and harvest management tactics can affect weed numbers, the weed seedbank and yields

• uses farm-specific management and site-specific weather

• is multi-species

See www.dpird.wa.gov.au for further information on Weed Seed Wizard.

This case …

"Deep Learning For Microscope Image Denoising", Nasreen Buhn, Sriya Adunur, Guy Hagen, Jonathan Ventura

College of Engineering Summer Undergraduate Research Program

In order to avoid damaging live cells, optical microscope imaging must be conducted under low-excitation light intensity and/or short exposure times, resulting in low signal-to-noise ratios (SNR). Deep learning methods offer an effective solution for removing microscope noise, utilizing algorithms that are able to reconstruct finer features in low SNR images. This research explores the denoising capability of several deep learning methods based on PSNR and SSIM. Tested methods include traditional approaches (BMED), supervised learning (CARE and Restormer), and unsupervised methods (Noise2Fast, N2V, SSD-Unsupervised, and SASSID). The Restormer model, which employs an encoder-decoder transformer architecture and progressive learning, stood out …

Equipping Future Physicians With Artificial Intelligence Competencies Through Student Associations, Spencer Hopson, Carson Mildon, Kyle Hassard, Paul Urie, Dennis Della Corte

Faculty Publications

Advances in artificial intelligence (AI) in the medical sector necessitate the development of AI literacy among future physicians. This article explores the pioneering efforts of the AI in Medicine Association (AIM) at Brigham Young University, which offers a framework for undergraduate pre-medical students to gain hands-on experience, receive principled education, explore ethical considerations, and learn appraisal of AI models. By supplementing formal, university-organized pre-medical education with a student-led, faculty-supported introduction to AI through an extracurricular academic association, AIM alleviates apprehensions regarding AI in medicine early and empowers students preparing for medical school to navigate the evolving landscape of AI in …