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.

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.

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.

Screening for hemoglobinopathies during routine hemoglobin A1c testing using the Tosoh G7 Glycohemoglobin Analyzer.

Approximately 5.1% of the US population has diabetes mellitus, and hemoglobin (Hb) A1c levels are routinely measured to monitor long-term glycemic control in these patients. Many laboratories use ion exchange chromatography for such measurements, and the presence of hemoglobin variants and hemoglobinopathies often results in abnormal peaks on the chromatogram. The goal of this study was to evaluate the potential that detection of these abnormal peaks provides as a screening tool for Hb variants and hemoglobinopathies. We examined 366 specimens with abnormal peaks observed during routine Hb A1c measurements using the G7 Glycohemoglobin Analyzer (Tosoh Bioscience, Inc.). Hb variants and hemoglobinopathies were characterized by alkaline and acid electrophoresis, solubility testing for Hb S, and clinical parameters. In 252 cases, sickle cell trait was identified with a mean retention time (RT) of 1.44 (SD +/-0.02) min. In 82 cases, Hb C trait was identified with a mean RT of 1.66 +/-0.03 min. RTs for other Hb abnormalities, including sickle cell disease, homozygous Hb C disease, C Harlem trait, alpha-chain Hb variants, Hb D trait, Hb G trait, Hb J trait, Hb Raleigh, and Hb Lepore were also determined. Our results demonstrate that routine Hb A1c testing provides a potential screening tool for the detection of common hemoglobin variants and hemoglobinopathies. The previously unreported RTs for the G7 Glycohemoglobin Analyzer are provided, which can facilitate further testing in previously undiagnosed patients and confirm the cause of abnormal peaks in patients with known hemoglobin abnormalities.