Computer Sciences Commons^™

Machine Learning Predicting Acute Pain And Opioid Dose In Radiation Treated Oropharyngeal Cancer Patients, Vivian Salama, Laia Humbert-Vidan, Brandon Godinich, Kareem A Wahid, Dina M Elhabashy, Mohamed A Naser, Renjie He, Abdallah S R Mohamed, Ariana J Sahli, Katherine A Hutcheson, Gary Brandon Gunn, David I Rosenthal, Clifton D Fuller, Amy C Moreno

Faculty, Staff and Student Publications

Introduction: Acute pain is common among oral cavity/oropharyngeal cancer (OCC/OPC) patients undergoing radiation therapy (RT). This study aimed to predict acute pain severity and opioid doses during RT using machine learning (ML), facilitating risk-stratification models for clinical trials.

Methods: A retrospective study examined 900 OCC/OPC patients treated with RT during 2017-2023. Pain intensity was assessed using NRS (0-none, 10-worst) and total opioid doses were calculated using morphine equivalent daily dose (MEDD) conversion factors. Analgesics efficacy was assessed using combined pain intensity and total MEDD. ML predictive models were developed and validated, including Logistic Regression (LR), Support Vector Machine (SVM), Random …

Machine Learning Methods For Pattern Recognition Analysis Of Genomic And Molecular Data, Kuang Du

Dissertations

While immune therapies achieve remarkable success in treating various cancers, only a subset of patients achieves a durable clinical response, and many exhibit innate or acquired resistance. Precision medicine aims to tailor treatments to individual patients based on specific biological markers, ensuring that each patient receives the therapy most likely to be effective. Predictive biomarkers and gene signatures offer potential for more personalized treatment strategies by identifying patients likely to benefit. Recent studies suggest that gene signatures, comprising sets of genes, hold predictive value for certain clinical variables. Typically derived from biological expert knowledge, these signatures demonstrate substantial predictive potential, …

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 …

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 …

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 …

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 …

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 …

"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 …

A Framework For Human Evaluation Of Large Language Models In Healthcare Derived From Literature Review, Thomas Yu Chow Tam, Sonish Sivarajkumar, Sumit Kapoor, Alisa V Stolyar, Katelyn Polanska, Karleigh R Mccarthy, Hunter Osterhoudt, Xizhi Wu, Shyam Visweswaran, Sunyang Fu, Piyush Mathur, Giovanni E Cacciamani, Cong Sun, Yifan Peng, Yanshan Wang

Faculty, Staff and Student Publications

With generative artificial intelligence (GenAI), particularly large language models (LLMs), continuing to make inroads in healthcare, assessing LLMs with human evaluations is essential to assuring safety and effectiveness. This study reviews existing literature on human evaluation methodologies for LLMs in healthcare across various medical specialties and addresses factors such as evaluation dimensions, sample types and sizes, selection, and recruitment of evaluators, frameworks and metrics, evaluation process, and statistical analysis type. Our literature review of 142 studies shows gaps in reliability, generalizability, and applicability of current human evaluation practices. To overcome such significant obstacles to healthcare LLM developments and deployments, we …

Image-Guided Patient-Specific Optimization Of Catheter Placement For Convection-Enhanced Nanoparticle Delivery In Recurrent Glioblastoma, Chengyue Wu, David A Hormuth, Chase D Christenson, Ryan T Woodall, Michael R A Abdelmalik, William T Phillips, Thomas J R Hughes, Andrew J Brenner, Thomas E Yankeelov

Faculty, Staff and Student Publications

Background: Proper catheter placement for convection-enhanced delivery (CED) is required to maximize tumor coverage and minimize exposure to healthy tissue. We developed an image-based model to patient-specifically optimize the catheter placement for rhenium-186 (186Re)-nanoliposomes (RNL) delivery to treat recurrent glioblastoma (rGBM).

Methods: The model consists of the 1) fluid fields generated via catheter infusion, 2) dynamic transport of RNL, and 3) transforming RNL concentration to the SPECT signal. Patient-specific tissue geometries were assigned from pre-delivery MRIs. Model parameters were personalized with either 1) individual-based calibration with longitudinal SPECT images, or 2) population-based assignment via leave-one-out cross-validation. The concordance correlation coefficient …

Multi-Scale Variational Autoencoder For Imputation Of Missing Values In Untargeted Metabolomics Using Whole-Genome Sequencing Data, Chen Zhao, Kuan-Jui Su, Chong Wu, Xuewei Cao, Qiuying Sha, Wu Li, Zhe Luo, Tian Qing, Chuan Qiu, Lan Juan Zhao, Anqi Liu, Lindong Jiang, Xiao Zhang, Hui Shen, Weihua Zhou, Hong-Wen Deng

Faculty, Staff and Student Publications

Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies.

Method: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics …

Molecular Docking, Pharmacological Profiling, And Molecular Dynamics Simulation Of Potential Antihyperuricemic Agent From Secondary Metabolites Of Dillenia Philippinensis Rolfe (Dilleniaceae), Louie Rince C. Suyo, John P. Paulin, Nicole Clarence Louise L. Gapaz, Markus Brent S. Arevalo, Vince Tyrell P. Yongco, Librado A. Santiago

Karbala International Journal of Modern Science

Crystal accumulation in the joints due to increased serum uric acid (sUA) may lead to an inflammatory condition called gout. Increased sUA is caused by the excessive reabsorption of the urate anion transporter-1 (URAT-1). Therefore, URAT-1 inhibition will promote uric acid excretion and reduce the risk of having gout. Dillenia philippinensis Rolfe, often known as katmon, is an endemic plant in the Philippines with bioactive compounds associated with several therapeutic benefits. The present study represents the first scientific inquiry into the antihyperuricemic potential of compounds isolated from D. philippinensis. This study aimed to assess the interaction of URAT-1 with …

Hyperpolarized Magnetic Resonance Imaging, Nuclear Magnetic Resonance Metabolomics, And Artificial Intelligence To Interrogate The Metabolic Evolution Of Glioblastoma, Kang Lin Hsieh, Qing Chen, Travis C Salzillo, Jian Zhang, Xiaoqian Jiang, Pratip K Bhattacharya, Shyan Shams

Faculty, Staff and Student Publications

Glioblastoma (GBM) is a malignant Grade VI cancer type with a median survival duration of only 8-16 months. Earlier detection of GBM could enable more effective treatment. Hyperpolarized magnetic resonance spectroscopy (HPMRS) could detect GBM earlier than conventional anatomical MRI in glioblastoma murine models. We further investigated whether artificial intelligence (A.I.) could detect GBM earlier than HPMRS. We developed a deep learning model that combines multiple modalities of cancer data to predict tumor progression, assess treatment effects, and to reconstruct in vivo metabolomic information from ex vivo data. Our model can detect GBM progression two weeks earlier than conventional MRIs …

Enhancing Clinical Relevance Of Pretrained Language Models Through Integration Of External Knowledge: Case Study On Cardiovascular Diagnosis From Electronic Health Records, Qiuhao Lu, Andrew Wen, Thien Nguyen, Hongfang Liu

Faculty, Staff and Student Publications

Background: Despite their growing use in health care, pretrained language models (PLMs) often lack clinical relevance due to insufficient domain expertise and poor interpretability. A key strategy to overcome these challenges is integrating external knowledge into PLMs, enhancing their adaptability and clinical usefulness. Current biomedical knowledge graphs like UMLS (Unified Medical Language System), SNOMED CT (Systematized Medical Nomenclature for Medicine-Clinical Terminology), and HPO (Human Phenotype Ontology), while comprehensive, fail to effectively connect general biomedical knowledge with physician insights. There is an equally important need for a model that integrates diverse knowledge in a way that is both unified and compartmentalized. …

Interpretable And Evidential Deep Learning For Medical Image Analysis, Sai Chandra Kosaraju

UNLV Theses, Dissertations, Professional Papers, and Capstones

Automatic histopathological Whole Slide Image (WSI) analysis has been highlighted along with the advancements in microscopic imaging techniques, but manual examination and diagnosis of WSIs are time-consuming and tiresome. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. Especially, Convolutional Neural Networks CNN models such as Inception and DenseNet have achieved effective performance. However, automatic histopathological WSI analysis still has significant drawbacks such as considering deep learning as black-box models, predicting disease independently on a small part of images (patch images) extracted from WSIs, limitations in predicting a single slide-based score for a patient, and capturing disease-specific morphology …

Querymate: A Custom Llm Powered By Llamacpp, Pegah Khosravi

Open Educational Resources

No abstract provided.

Innovation Path At Institute For Protein Design Of Washington University And Its Enlightenment For Construction Of New Life Sciences R&D Institutions, Runzhou Zhao, Ming Ni, Yunzhi Fa, Xiaochen Bo, Jian Jiao

Bulletin of Chinese Academy of Sciences (Chinese Version)

The Institute for Protein Design (IPD) at the University of Washington is a pioneering local and state-supported non-profit scientific research institution. Since its establishment in 2012, IPD has seized the opportunity of AI for Science and open science, and continuously enhanced its capabilities of fundamental innovations, breakthrough technologies, and industrial impact. We summarized five factors contributing to IPD’s development, including focusing on the cutting-edge issues of basic scientific research to gain a first-mover advantage and then further expand, integrating AI-enhanced digital tools and solid experimental validations, facilitating the integrated development of innovation and industrial chains, giving full play to the …

Identification And Characterization Of Intrinsically Disordered Protein Regions, Guy Wayne Dayhoff Ii

USF Tampa Graduate Theses and Dissertations

This dissertation investigates protein intrinsic disorder and intrinsically disordered protein regions (IDPRs) through the development and application of advanced computational and experimental techniques. Chapter 1 provides an introduction to protein intrinsic disorder, outlining the historical context and fundamental concepts that highlight the importance of intrinsically disordered proteins (IDPs) and IDPRs in various biological processes. Chapter 2 focuses on the rapid prediction and analysis of protein intrinsic disorder. We introduce RIDAO (Rapid Intrinsic Disorder Analysis Online), a high-efficiency web-based tool that integrates multiple disorder predictors. RIDAO significantly outperforms existing predictors in computational efficiency, making it suitable for large-scale proteomic studies. We …

Streaminghub - A Realtime Biosignal Processing Framework For Lab Scale Experimentation, Yasith Jayawardana

Computer Science Theses & Dissertations

In human subjects research, biosignals such as eye movements, heart rate, and brain activity, are often collected and analyzed to find patterns with tangible real-world implications. Modern advancements in technology have sparked interest towards analyzing biosignals in realtime. When developing such algorithms, one may expect to find free, open-source tools that provide easy access to live, recorded, and simulated data streams. Yet, biosignal interfaces are often vendor-specific, making cross-vendor biosignal streaming non-trivial. Likewise, reading biosignal datasets is also non-trivial, as their content may be arranged quite differently.

To combat this divide, we provide the scientific community with a realtime biosignal …

A Deep Learning Method To Predict Bacterial Adp-Ribosyltransferase Toxins, Dandan Zheng, Siyu Zhou, Lihong Chen, Guansong Pang, Jian Yang

Research Collection School Of Computing and Information Systems

Motivation: ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent virulence factors that orchestrate the manipulation of host cell functions to facilitate bacterial pathogenesis. Despite their pivotal role, the bioinformatic identification of novel bARTTs poses a formidable challenge due to limited verified data and the inherent sequence diversity among bARTT members. Results: We proposed a deep learning-based model, ARTNet, specifically engineered to predict bARTTs from bacterial genomes. Initially, we introduced an effective data augmentation method to address the issue of data scarcity …

Advances In Data-Driven Life Sciences Research, Haiping Jiang, Chunchun Gao, Wenhao Liu, Yungui Yang, Xin Li

Bulletin of Chinese Academy of Sciences (Chinese Version)

The field of life sciences is rapidly evolving, driven by advancements in experimental techniques and vast biological big data which gradually arise and play an increasingly important role in life science research. First of all, biological big data has diversity and complexity, including genomic data, epigenomic data, proteomic data and other types. These data provide researchers with more comprehensive information and help reveal the laws behind life phenomena. Second, new data-driven developments and applications in life sciences cover many fields such as gene editing, precision medicine, drug development, etc., providing unprecedented possibilities for human health and quality of life. However, …

Multithreaded Applications On The Heterogeneous Research Computing Environment., Sungbo Jung

Electronic Theses and Dissertations

Bioinformatics is a domain that has experienced rapid research growth in recent years, as evidenced by the increasing number of articles in biomedical databases such as PubMed, which adds over a million publications every year. However, this also poses a challenge for researchers who need to find relevant citations for their work. Therefore, developing efficient indexing and searching methods for text data is crucial for Bioinformatics. One key technique for information retrieval is document inversion, which involves creating an inverted index to enable efficient searching through vast collections of text or documents. This Ph.D. research aims to design the research …

Artificial Intelligence-Powered Assessment Of Pathologic Response To Neoadjuvant Atezolizumab In Patients With Nsclc: Results From The Lcmc3 Study, Sanja Dacic, William D Travis, Jennifer M Giltnane, Filip Kos, John Abel, Stephanie Hilz, Junya Fujimoto, Lynette Sholl, Jon Ritter, Farah Khalil, Yi Liu, Amaro Taylor-Weiner, Murray Resnick, Hui Yu, Fred R Hirsch, Paul A Bunn, David P Carbone, Valerie Rusch, David J Kwiatkowski, Bruce E Johnson, Jay M Lee, Stephanie R Hennek, Ilan Wapinski, Alan Nicholas, Ann Johnson, Katja Schulze, Mark G Kris, Ignacio I Wistuba

Faculty, Staff and Student Publications

Introduction: Pathologic response (PathR) by histopathologic assessment of resected specimens may be an early clinical end point associated with long-term outcomes with neoadjuvant therapy. Digital pathology may improve the efficiency and precision of PathR assessment. LCMC3 (NCT02927301) evaluated neoadjuvant atezolizumab in patients with resectable NSCLC and reported a 20% major PathR rate.

Methods: We determined PathR in primary tumor resection specimens using guidelines-based visual techniques and developed a convolutional neural network model using the same criteria to digitally measure the percent viable tumor on whole-slide images. Concordance was evaluated between visual determination of percent viable tumor (n = …

Time Series Models For Predicting Application Gpu Utilization And Power Draw Based On Trace Data, Dorothy Xiaoshuang Parry

Electrical & Computer Engineering Theses & Dissertations

This work explores collecting performance metrics and leveraging various statistical and machine learning time series predictive models on a memory-intensive application, Inception v3. Trace data collected using nvidia-smi measured GPU utilization and power draw for two runs of Inception3. Experimental results from the statistical and machine learning-based time series predictive algorithms showed that the predictions from statistical-based models were unable to capture the complex changes in the trace data. The Probabilistic TNN model provided the best results for the power draw trace, according to the test evaluation metrics. For the GPU utilization trace, the RNN models produced the most accurate …

Cancergpt For Few Shot Drug Pair Synergy Prediction Using Large Pretrained Language Models, Tianhao Li, Sandesh Shetty, Advaith Kamath, Ajay Jaiswal, Xiaoqian Jiang, Ying Ding, Yejin Kim

Faculty, Staff and Student Publications

Large language models (LLMs) have been shown to have significant potential in few-shot learning across various fields, even with minimal training data. However, their ability to generalize to unseen tasks in more complex fields, such as biology and medicine has yet to be fully evaluated. LLMs can offer a promising alternative approach for biological inference, particularly in cases where structured data and sample size are limited, by extracting prior knowledge from text corpora. Here we report our proposed few-shot learning approach, which uses LLMs to predict the synergy of drug pairs in rare tissues that lack structured data and features. …

A Reliable Diabetic Retinopathy Grading Via Transfer Learning And Ensemble Learning With Quadratic Weighted Kappa Metric, Sai Venkatesh Chilukoti, Liqun Shan, Vijay Srinivas Tida, Anthony S. Maida, Xiali Hei

Computer Science Faculty Publications

The most common eye infection in people with diabetes is diabetic retinopathy (DR). It might cause blurred vision or even total blindness. Therefore, it is essential to promote early detection to prevent or alleviate the impact of DR. However, due to the possibility that symptoms may not be noticeable in the early stages of DR, it is difficult for doctors to identify them. Therefore, numerous predictive models based on machine learning (ML) and deep learning (DL) have been developed to determine all stages of DR. However, existing DR classification models cannot classify every DR stage or use a computationally heavy …

A New Paradigm Of Life Science Research Driven By Artificial Intelligence, Xin Li, Hanchao Yu

Bulletin of Chinese Academy of Sciences (Chinese Version)

The rapid development of biotechnology and information technology has brought life sciences into a new era of data explosion. The traditional life science research paradigm struggles to reveal the fundamental rules of complex biological systems from rapidly growing biological big data. As artificial intelligence continues to achieve disruptive breakthroughs in life science, a new paradigm driven by AI is emerging. This study delves into typical examples of life science research driven by AI, proposes the concept and key elements of the new life science research paradigm, elaborates on the cutting-edge of life science research under this new paradigm, and discusses …

Enabling The Clinical Application Of Artificial Intelligence In Genomics: A Perspective Of The Amia Genomics And Translational Bioinformatics Workgroup, Nephi A Walton, Radha Nagarajan, Chen Wang, Murat Sincan, Robert R Freimuth, David B Everman, Derek C Walton, Scott P Mcgrath, Dominick J Lemas, Panayiotis V Benos, Alexander V Alekseyenko, Qianqian Song, Ece Gamsiz Uzun, Casey Overby Taylor, Alper Uzun, Thomas Nate Person, Nadav Rappoport, Zhongming Zhao, Marc S Williams

Faculty, Staff and Student Publications

OBJECTIVE: Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space.

PROCESS: A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members.

CONCLUSIONS: Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of …

When Brain Meets Artificial Intelligence, Lu Zhang

Computer Science and Engineering Dissertations - Archive

When we review the history of development of artificial intelligence (AI), we will find that brain science plays a pivotal role in fostering breakthroughs in AI, such as artificial neural networks (ANNs). Today, AI has made remarkable strides, particularly with the emergence of large language models (LLMs), surpassing expectations and achieving human-level performance in certain tasks. Nonetheless, an insurmountable gap remains between AI and human intelligence. It is urgent to establish a bridge between brain science and AI, promoting their mutual enhancement and collaborations. This involve establishing connections from brain science to AI (brain-inspired AI), and reversely, from AI to …