Implementing Trustworthy AI in VA High Reliability Health Care Organizations.

Background: Artificial intelligence (AI) has great potential to improve health care quality, safety, efficiency, and access. However, the widespread adoption of health care AI needs to catch up to other sectors. Challenges, including data limitations, misaligned incentives, and organizational obstacles, have hindered implementation. Strategic demonstrations, partnerships, aligned incentives, and continued investment are needed to enable responsible adoption of AI. High reliability health care organizations offer insights into safely implementing major initiatives through frameworks like the Patient Safety Adoption Framework, which provides practical guidance on leadership, culture, process, measurement, and person-centeredness to successfully adopt safety practices. High reliability health care organizations ensure consistently safe and high quality care through a culture focused on reliability, accountability, and learning from errors and near misses. Observations: The Veterans Health Administration applied a high reliability health care model to instill safety principles and improve outcomes. As the use of AI becomes more widespread, ensuring its ethical development is crucial to avoiding new risks and harm. The US Department of Veterans Affairs National AI Institute proposed a Trustworthy AI Framework tailored for federal health care with 6 principles: purposeful, effective and safe, secure and private, fair and equitable, transparent and explainable, and accountable and monitored. This aims to manage risks and build trust. Conclusions: Combining these AI principles with high reliability safety principles can enable successful, trustworthy AI that improves health care quality, safety, efficiency, and access. Overcoming AI adoption barriers will require strategic efforts, partnerships, and investment to implement AI responsibly, safely, and equitably based on the health care context.

Applications of ChatGPT and Large Language Models in Medicine and Health Care: Benefits and Pitfalls.

Background: The use of large language models like ChatGPT is becoming increasingly popular in health care settings. These artificial intelligence models are trained on vast amounts of data and can be used for various tasks, such as language translation, summarization, and answering questions. Observations: Large language models have the potential to revolutionize the industry by assisting medical professionals with administrative tasks, improving diagnostic accuracy, and engaging patients. However, pitfalls exist, such as its inability to distinguish between real and fake information and the need to comply with privacy, security, and transparency principles. Conclusions: Careful consideration is needed to ensure the responsible and ethical use of large language models in medicine and health care. The development of [artificial intelligence] is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone. It will change the way people work, learn, travel, get health care, and communicate with each other. Bill Gates1.

Revolutionizing Digital Pathology With the Power of Generative Artificial Intelligence and Foundation Models.

Digital pathology has transformed the traditional pathology practice of analyzing tissue under a microscope into a computer vision workflow. Whole-slide imaging allows pathologists to view and analyze microscopic images on a computer monitor, enabling computational pathology. By leveraging artificial intelligence (AI) and machine learning (ML), computational pathology has emerged as a promising field in recent years. Recently, task-specific AI/ML (eg, convolutional neural networks) has risen to the forefront, achieving above-human performance in many image-processing and computer vision tasks. The performance of task-specific AI/ML models depends on the availability of many annotated training datasets, which presents a rate-limiting factor for AI/ML development in pathology. Task-specific AI/ML models cannot benefit from multimodal data and lack generalization, eg, the AI models often struggle to generalize to new datasets or unseen variations in image acquisition, staining techniques, or tissue types. The 2020s are witnessing the rise of foundation models and generative AI. A foundation model is a large AI model trained using sizable data, which is later adapted (or fine-tuned) to perform different tasks using a modest amount of task-specific annotated data. These AI models provide in-context learning, can self-correct mistakes, and promptly adjust to user feedback. In this review, we provide a brief overview of recent advances in computational pathology enabled by task-specific AI, their challenges and limitations, and then introduce various foundation models. We propose to create a pathology-specific generative AI based on multimodal foundation models and present its potentially transformative role in digital pathology. We describe different use cases, delineating how it could serve as an expert companion of pathologists and help them efficiently and objectively perform routine laboratory tasks, including quantifying image analysis, generating pathology reports, diagnosis, and prognosis. We also outline the potential role that foundation models and generative AI can play in standardizing the pathology laboratory workflow, education, and training.

Predicting ward transfer mortality with machine learning.

In order to address a long standing challenge for internal medicine physicians we developed artificial intelligence (AI) models to identify patients at risk of increased mortality. After querying 2,425 records of patients transferred from non-intensive care units to intensive care units from the Veteran Affairs Corporate Data Warehouse (CDW), we created two datasets. The former used 22 independent variables that included "Length of Hospital Stay" and "Days to Intensive Care Transfer," and the latter lacked these two variables. Since these two variables are unknown at the time of admission, the second set is more clinically relevant. We trained 16 machine learning models using both datasets. The best-performing models were fine-tuned and evaluated. The LightGBM model achieved the best results for both datasets. The model trained with 22 variables achieved a Receiver Operating Characteristics Curve-Area Under the Curve (ROC-AUC) of 0.89 and an accuracy of 0.72, with a sensitivity of 0.97 and a specificity of 0.68. The model trained with 20 variables achieved a ROC-AUC of 0.86 and an accuracy of 0.71, with a sensitivity of 0.94 and a specificity of 0.67. The top features for the former model included "Total length of Stay," "Admit to ICU Transfer Days," and "Lymphocyte Next Lab Value." For the latter model, the top features included "Lymphocyte First Lab Value," "Hemoglobin First Lab Value," and "Hemoglobin Next Lab Value." Our clinically relevant predictive mortality model can assist providers in optimizing resource utilization when managing large caseloads, particularly during shift changes.

Artificial Intelligence for Clinical Flow Cytometry.

In this review, the authors discuss the fundamental principles of machine learning. They explore recent studies and approaches in implementing machine learning into flow cytometry workflows. These applications are promising but not without their shortcomings. Explainability may be the biggest barrier to adoption, as they contain "black boxes" in which a complex network of mathematical processes learns features of data that are not translatable into real language. The authors discuss the current limitations of machine learning models and the possibility that, without a multiinstitutional development process, these applications could have poor generalizability. They also discuss widespread deployment of augmented decision-making.

Pilot Lightweight Denoising Algorithm for Multiple Sclerosis on Spine MRI.

Multiple sclerosis (MS) is a severely debilitating disease which requires accurate and timely diagnosis. MRI is the primary diagnostic vehicle; however, it is susceptible to noise and artifact which can limit diagnostic accuracy. A myriad of denoising algorithms have been developed over the years for medical imaging yet the models continue to become more complex. We developed a lightweight algorithm which utilizes the image's inherent noise via dictionary learning to improve image quality without high computational complexity or pretraining through a process known as orthogonal matching pursuit (OMP). Our algorithm is compared to existing traditional denoising algorithms to evaluate performance on real noise that would commonly be encountered in a clinical setting. Fifty patients with a history of MS who received 1.5 T MRI of the spine between the years of 2018 and 2022 were retrospectively identified in accordance with local IRB policies. Native resolution 5 mm sagittal images were selected from T2 weighted sequences for evaluation using various denoising techniques including our proposed OMP denoising algorithm. Peak signal to noise ratio (PSNR) and structural similarity index (SSIM) were measured. While wavelet denoising demonstrated an expected higher PSNR than other models, its SSIM was variable and consistently underperformed its comparators (0.94 ± 0.10). Our pilot OMP denoising algorithm provided superior performance with greater consistency in terms of SSIM (0.99 ± 0.01) with similar PSNR to non-local means filtering (NLM), both of which were superior to other comparators (OMP 37.6 ± 2.2, NLM 38.0 ± 1.8). The superior performance of our OMP denoising algorithm in comparison to traditional models is promising for clinical utility. Given its individualized and lightweight approach, implementation into PACS may be more easily incorporated. It is our hope that this technology will provide improved diagnostic accuracy and workflow optimization for Neurologists and Radiologists, as well as improved patient outcomes.

Establishing a Hospital Artificial Intelligence Committee to Improve Patient Care.

Background: The use of artificial intelligence (AI) in health care is increasing and has shown utility in many medical specialties, especially pathology, radiology, and oncology. Observations: Many barriers exist to successfully implement AI programs in the clinical setting. To address these barriers, a formal governing body, the hospital AI Committee, was created at James A. Haley Veterans' Hospital in Tampa, Florida. The AI committee reviews and assesses AI products based on their success at protecting human autonomy; promoting human well-being and safety and the public interest; ensuring transparency, explainability, and intelligibility; fostering responsibility and accountability; ensuring inclusiveness and equity; and promoting AI that is responsive and sustainable. Conclusions: Through the hospital AI Committee, we may overcome many obstacles to successfully implementing AI applications in the clinical setting.

Cervical Cancer Screening in South Florida Veteran Population, 2014 to 2020: Cytology and High-Risk Human Papillomavirus Correlation and Epidemiology.

Objective This project aims to use our robust women's health patient data to analyze the correlation between cytology and high-risk human papillomavirus (Hr-HPV) testing, study the performance of Hr-HPV testing for detecting cytology lesions, and examine epidemiologic measures of human papillomavirus (HPV) infections in the women's veteran population. Methods We collected patient data from 2014 to 2020 from our computerized patient record system. We performed HPV assays using the cobas® 4800 system (Roche Diagnostics, Basel, Switzerland). The cobas HPV assay detects HPV 16, HPV 18, and 12 other HPV types (31, 33, 35, 39, 45, 51, 56, 58, 59, 66, and 68). We organized cytology results and Hr-HPV assays with Microsoft Access and Microsoft Excel (Microsoft Corporation, Washington, USA) for analysis. Results A total of 9437 cervical specimens were co-tested. High-grade cytology lesions - high-grade intraepithelial lesion (HSIL) or higher and atypical squamous cells, cannot exclude HSIL (ASC-H) - were overwhelmingly positive for Hr-HPV (94.1% and 87.2%, respectively). Low-grade cytology lesions - low-grade squamous intraepithelial lesion ((LSIL) and atypical squamous cells of undetermined significance (ASC-US) - were positive for Hr-HPV in lower percentages (72.6% and 54.9%, respectively). Hr-HPV testing had a sensitivity of 91.3%, a specificity of 93.1%, a positive predictive value of 16.4%, and a negative predictive value of 99.8% for detecting high-grade cytology lesions. Hr-HPV testing had a lower performance for detecting low-grade cytology lesions. Ten cases had high-grade cytology and negative Hr-HPV test. Out of 10 such patients, nine showed no dysplasia (six) or low-grade dysplasia (three) on subsequent biopsy. Overall, 14.4% of tests were positive for Hr-HPV. The highest positive Hr-HPV test rates were in the third and eighth decades of life, 25.1% and 22.0%, respectively. However, the eighth decade consisted of a small sample of only 50 women. In women over 30 years of age with Hr-HPV infections, HPV types 16 and 18 were present in 11.7% and 6.4% of tests, respectively. Other HPV types were present in 82.3% of tests. Conclusions Hr-HPV testing has a high performance in detecting high-grade cytology lesions and a lower performance for detecting low-grade cytology lesions. However, studies show that LSIL rarely progresses to cervical intraepithelial neoplasia grade 3 or higher (CIN3+), suggesting minimal to no impact on cervical cancer screening. We believe our findings are in accordance with recent studies and affirm the guidelines that recommend primary Hr-HPV testing as the preferred screening method. The percentage of positive Hr-HPV tests and rates for age and HPV types 16 and 18 in our women's veteran population suggest similar HPV prevalence to that of the general US population.

Artificial Intelligence: Review of Current and Future Applications in Medicine.

BACKGROUND: The role of artificial intelligence (AI) in health care is expanding rapidly. Currently, there are at least 29 US Food and Drug Administration-approved AI health care devices that apply to numerous medical specialties and many more are in development. OBSERVATIONS: With increasing expectations for all health care sectors to deliver timely, fiscally-responsible, high-quality health care, AI has potential utility in numerous areas, such as image analysis, improved workflow and efficiency, public health, and epidemiology, to aid in processing large volumes of patient and medical data. In this review, we describe basic terminology, principles, and general AI applications relating to health care. We then discuss current and future applications for a variety of medical specialties. Finally, we discuss the future potential of AI along with the potential risks and limitations of current AI technology. CONCLUSIONS: AI can improve diagnostic accuracy, increase patient safety, assist with patient triage, monitor disease progression, and assist with treatment decisions.

Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis.

BACKGROUND: Coronavirus disease-19 (COVID-19), caused by a novel member of the coronavirus family, is a respiratory disease that rapidly reached pandemic proportions with high morbidity and mortality. In only a few months, it has had a dramatic impact on society and world economies. COVID-19 has presented numerous challenges to all aspects of health care, including reliable methods for diagnosis, treatment, and prevention. Initial efforts to contain the spread of the virus were hampered by the time required to develop reliable diagnostic methods. Artificial intelligence (AI) is a rapidly growing field of computer science with many applications for health care. Machine learning is a subset of AI that uses deep learning with neural network algorithms. It can recognize patterns and achieve complex computational tasks often far quicker and with increased precision than can humans. METHODS: In this article, we explore the potential for the simple and widely available chest X-ray (CXR) to be used with AI to diagnose COVID-19 reliably. Microsoft CustomVision is an automated image classification and object detection system that is a part of Microsoft Azure Cognitive Services. We utilized publicly available CXR images for patients with COVID-19 pneumonia, pneumonia from other etiologies, and normal CXRs as a dataset to train Microsoft CustomVision. RESULTS: Our trained model overall demonstrated 92.9% sensitivity (recall) and positive predictive value (precision), with results for each label showing sensitivity and positive predictive value at 94.8% and 98.9% for COVID-19 pneumonia, 89% and 91.8% for non-COVID-19 pneumonia, 95% and 88.8% for normal lung. We then validated the program using CXRs of patients from our institution with confirmed COVID-19 diagnoses along with non-COVID-19 pneumonia and normal CXRs. Our model performed with 100% sensitivity, 95% specificity, 97% accuracy, 91% positive predictive value, and 100% negative predictive value. CONCLUSIONS: We have used a readily available, commercial platform to demonstrate the potential of AI to assist in the successful diagnosis of COVID-19 pneumonia on CXR images. The findings have implications for screening and triage, initial diagnosis, monitoring disease progression, and identifying patients at increased risk of morbidity and mortality. Based on the data, a website was created to demonstrate how such technologies could be shared and distributed to others to combat entities such as COVID-19 moving forward.

Comparing Artificial Intelligence Platforms for Histopathologic Cancer Diagnosis.

Two machine learning platforms were successfully used to provide diagnostic guidance in the differentiation between common cancer conditions in veteran populations.

Warfarin pharmacogenomics: recommendations with available patented clinical technologies.

Warfarin pharmacogenomic testing has become a prime example of the utility of personalized molecular testing in the modern clinical laboratory. Warfarin is a commonly used drug for the prevention and treatment of thromboembolic complications in a variety of clinical situations. However, a number of factors lead to a high interindividual variability in dose requirements. Among the primary factors in this variability are genetic polymorphisms in general patient populations, which can account for 35-50% of varying dose requirements among patients. In this review, we discuss the implications of polymorphisms in the cytochrome P-450 enzyme 2C9 (CYP2C9) and Vitamin K Epoxide Reductase Enzyme Complex subunit 1 (VKORC1) as they relate to therapeutic warfarin dosing. We discuss the clinical utility of pharmacogenomics testing as related to warfarin dosing, and propose a clinical model for the implementation of the pharmacogenomic test results. Finally, we provide a brief overview of the currently available commercial testing platforms with discussion of the complexities of utilizing patented methodologies in bringing genetic testing such as this to the clinical laboratory.

Hepatitis C genotype analysis: results in a large veteran population with review of the implications for clinical practice.

CONTEXT: Hepatitis C virus (HCV) infects up to 1.8% of the US general population, although the rate is significantly higher in military veterans at 5.4-20%. Early detection and accurate diagnosis are critical as chronic HCV infection can lead to liver cirrhosis and hepatocellular carcinoma. Genotype analysis has both therapeutic and prognostic importance in patients with HCV infections. OBJECTIVE: We compare two versions of a commonly utilized platform for genotype analysis in HCV infections and review the implications of the results for clinical practice. DESIGN: A retrospective review of 9401 genotype results from 2001-2010 were analyzed. All results were obtained from the James A. Haley VA Medical Center, a large referral veterans' healthcare facility. RESULTS: Genotype 1 was identified in 80.1% of samples, genotype 2 in 11.1%, genotype 3 in 7.4%, and genotype 4 in 1.2%. Genotypes 5 and 6 were rarely present in our patient population. Improvements in diagnostic methodologies over the study period resulted in shifts in genotype subtyping. Specifically, upgrading from the Versant HCV genotype assay (LIPA) (Siemens, Tarrytown, NY) to the newer version 2.0 assay resulted in an increase in identification of genotype 1a by 18.5%. CONCLUSIONS: Improved technologies lead to accurate genotype identification and subtyping, both of which have increasingly important prognostic and therapeutic implications. The clinical importance of these results in patients with HCV infections is reviewed.

Granulomatous prostatitis linked to HLA-DRB1*1501.

PURPOSE: Granulomatous prostatitis is characterized by a pattern of granulomatous inflammation in the prostate. In most cases the etiology is unknown. Based on the hypothesis that granulomatous prostatitis may be an autoimmune disease we performed intermediate and selective high resolution typing of HLA-DR in a group of patients with the disease and compared the frequency of class II HLA phenotypes to that in a control group of volunteer marrow donors in the military. MATERIALS AND METHODS: Histological records from 1 institution from 1990 to 2000 revealed 12 patients with diffuse granulomatous prostatitis. Three patients were dead and 1 refused blood drawing. Peripheral blood from the remaining 8 patients was typed along with blood from an additional 3 identified at the practice of one of us from 1999 through 2002. All slides were reviewed by 1 pathologist. Intermediate resolution typing of HLA-A, B and DR was performed by polymerase chain reaction-sequence specific oligonucleotide probe. High resolution, allele specific identification of HLA DR15 was performed if patients were DR15 positive by intermediate resolution typing. RESULTS: There were 3 black and 8 white individuals identified with diffuse nonspecific granulomatous prostatitis. Six of 8 white patients (75%) were HLA-DR15 by intermediate resolution typing. One of the 3 black American patients (33%) was HLA-DR15. In the control group 127 of 451 white (28.2%) and 23 of 89 black (25.8%) volunteer marrow donors were HLA-DR15. The case-control comparison of white patients was significantly different (Fisher's exact test p = 0.0086). There were no statistically significant differences between case-control comparisons for any other HLA-DR phenotype. High resolution DR15 typing showed that the white patients were HLA-DRB1*1501 and the black patient was HLA-DRB1*1503. CONCLUSIONS: The data suggest an association between HLA-DRB1*1501 and granulomatous prostatitis. HLA-DR15 is strongly associated with other autoimmune diseases, notably multiple sclerosis. The data are consistent with an autoimmune etiology for nonspecific granulomatous prostatitis.