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.

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.

Neuroimaging in the Era of Artificial Intelligence: Current Applications.

Background: Artificial intelligence (AI) in medicine has shown significant promise, particularly in neuroimaging. AI increases efficiency and reduces errors, making it a valuable resource for physicians. With the increasing amount of data processing and image interpretation required, the ability to use AI to augment and aid the radiologist could improve the quality of patient care. Observations: AI can predict patient wait times, which may allow more efficient patient scheduling. Additionally, AI can save time for repeat magnetic resonance neuroimaging and reduce the time spent during imaging. AI has the ability to read computed tomography, magnetic resonance imaging, and positron emission tomography with reduced or without contrast without significant loss in sensitivity for detecting lesions. Neuroimaging does raise important ethical considerations and is subject to bias. It is vital that users understand the practical and ethical considerations of the technology. Conclusions: The demonstrated applications of AI in neuroimaging are numerous and varied, and it is reasonable to assume that its implementation will increase as the technology matures. AI's use for detecting neurologic conditions holds promise in combatting ever increasing imaging volumes and providing timely diagnoses.

Role of 3D Printing and Modeling to Aid in Neuroradiology Education for Medical Trainees.

BACKGROUND: Applications of 3-dimensional (3D) printing in medical imaging and health care are expanding. Currently, primary uses involve presurgical planning and patient and medical trainee education. Neuroradiology is a complex subdiscipline of radiology that requires further training beyond radiology residency. This review seeks to explore the clinical value of 3D printing and modeling specifically in enhancing neuroradiology education for radiology physician residents and medical trainees. METHODS: A brief review summarizing the key steps from radiologic image to 3D printed model is provided, including storage of computed tomography and magnetic resonance imaging data as digital imaging and communications in medicine files; conversion to standard tessellation language (STL) format; manipulation of STL files in interactive medical image control system software (Materialise) to create 3D models; and 3D printing using various resins via a Formlabs 2 printer. RESULTS: For the purposes of demonstration and proof of concept, neuroanatomy models deemed crucial in early radiology education were created via open-source hardware designs under free or open licenses. 3D-printed objects included a sphenoid bone, cerebellum, skull base, middle ear labyrinth and ossicles, mandible, circle of Willis, carotid aneurysm, and lumbar spine using a combination of clear, white, and elastic resins. CONCLUSIONS: Based on this single-institution experience, 3D-printed complex neuroanatomical structures seem feasible and may enhance resident education and patient safety. These same steps and principles may be applied to other subspecialties of radiology. Artificial intelligence also has the potential to advance the 3D process.

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.

Interpretable Artificial Intelligence for COVID-19 Diagnosis from Chest CT Reveals Specificity of Ground-Glass Opacities.

Background The use of CT imaging enhanced by artificial intelligence to effectively diagnose COVID-19, instead of or in addition to reverse transcription-polymerase chain reaction (RT-PCR), can improve widespread COVID-19 detection and resource allocation. Methods 904 axial lung window CT slices from 338 patients in 17 countries were collected and labeled. The data included 606 images from COVID-19 positive patients (confirmed via RT-PCR), 224 images of a variety of other pulmonary diseases including viral pneumonias, and 74 images of normal patients. We developed, trained, validated, and tested an object detection model which detects features in three categories: ground-glass opacities (GGOs) for COVID-19, GGOs for non-COVID-19 diseases, and features that are inconsistent with a COVID-19 diagnosis. These collected features are passed into an interpretable decision tree model to make a suggested diagnosis. Results On an independent test of 219 images from COVID-19 positive, a variety of pneumonia, and healthy patients, the model predicted COVID-19 diagnoses with an accuracy of 96.80 % (95% confidence interval [CI], 96.75 to 96.86) , AUC-ROC of 0.9664 (95% CI, 0.9659 to 0.9671) , sensitivity of 98.33% (95% CI, 98.29 to 98.40) , precision of 95.93% (95% CI, 95.83 to 95.99), and specificity of 94.95% (95% CI, 94.84 to 95.05). On an independent test of 34 images from asymptomatic COVID-19 positive patients, our model achieved an accuracy of 97.06% (95% CI, 96.81 to 97.06) and a sensitivity of 96.97% (95% CI, 96.71 to 96.97). Similarly high performance was also obtained for out-of-sample countries, and no significant performance difference was obtained between genders. Conclusion We present an interpretable artificial intelligence CT analysis tool to diagnose COVID-19 in both symptomatic and asymptomatic patients. Further, our model is able to differentiate COVID-19 GGOs from similar pathologies suggesting that GGOs can be disease-specific.

Atypical Neuroimaging Manifestations of Linear Scleroderma "en coup de sabre".

Linear scleroderma "en coup de sabre" is a subset of localized scleroderma with band-like sclerotic lesions typically involving the fronto-parietal regions of the scalp. Patients often present with neurologic symptoms. On imaging, patients may have lesions in the cerebrum ipsilateral to the scalp abnormality. Infratentorial lesions and other lesions not closely associated with the overlying scalp abnormality, such as those found in the cerebellum, have been reported, but are extremely uncommon. We present a case of an 8-year-old boy with a left fronto-parietal "en coup de sabre" scalp lesion and describe the neuroimaging findings of a progressively enlarging left cerebellar lesion discovered incidentally on routine magnetic resonance imaging. Interestingly, the patient had no neurologic symptoms given the size of the mass identified.

Neuroimaging and renal ultrasound manifestations of Oculocerebrorenal syndrome of Lowe.

Oculocerebrorenal syndrome of Lowe (OCRL) is a multisystem disorder characterized by congenital cataracts, hypotonia, and cognitive developmental delay with renal complications developing in the first few months of life. Clinical and laboratory findings of Lowe syndrome are well documented. Though a small number of case reports describe the neuroimaging features and the renal ultrasound manifestations of this disease, a comprehensive review of all the imaging manifestations has not been reported. The authors present a case of OCRL and review the neuroimaging and renal ultrasound manifestations of this multisystem disease.

Cauda equina involvement in Susac's syndrome.

Susac's syndrome is a rare autoimmune microangiopathy characterized by the clinical triad of encephalopathy, branch retinal artery occlusions, and sensorineural hearing loss. In many cases, the clinical triad is not fully present at the onset of symptoms. MRI studies often show characteristic punched out lesions of the central fibers of the corpus callosum, and leptomeningeal enhancement and deep gray matter lesions may also be seen. Here we present a case of Susac's syndrome in a middle aged man with the unique clinical finding of cauda equina syndrome and spinal MRI showing diffuse lumbosacral nerve root enhancement. Biopsy specimens of the brain, leptomeninges, and skin showed evidence of a pauci-immune endotheliopathy, consistent with pathology described in previous cases of Susac's syndrome. This case is important not only because it expands the clinical features of Susac's syndrome but also because it clarifies the mechanism of a disorder of the endothelium, an important target for many disorders of the nervous system.

Diffuse cauda equina enhancement in a middle aged male with Susac syndrome and symptomatic cauda equina syndrome.

Susac syndrome is a rare neurologic disorder first described by Susac et al. in 1979. Clinically, Susac syndrome consists of a triad including encephalopathy, branch retinal artery occlusions and sensorineural hearing loss. All three components of the triad usually do not present at the same time, thus delaying time to diagnosis. MRI studies often show characteristic punched out lesions of the central fibers of the corpus callosum. Intracranial leptomeningeal enhancement may be seen, however, cauda equina involvement has not been described to our knowledge. We present a case of Susac syndrome in a middle-aged male with symptoms of cauda equina syndrome, and spinal MRI showing diffuse enhancement of the nerve roots of the cauda equina.

Vascular steal of the portal vein after orthotopic liver transplant: intraoperative sonographic diagnosis.

Spontaneous splenorenal shunts (SSRSs) are portosystemic connections between the splenic vein and the left renal vein (LRV) that develop commonly in patients with portal hypertension. (1) They reportedly occur in 18% to 19% of patients evaluated for a liver transplant. (2),(3) As the liver become more cirrhotic, a major steal phenomenon may occur, whereby blood is shunted from the high-resistance venous bed of the liver to the lower systemic pressure of the LRV. (4) Not infrequently, an SSRS will go undetected during orthotopic liver transplantation because dissection is limited to the right upper quadrant. The importance of these shunts may be underappreciated preoperatively by the radiologist. Usually, if small, these shunts will involute without incident when the lower-resistance allograft is implanted. (5),(6) Larger varices, those greater than 10 mm at the level of transition into the LRV, are more likely to steal blood from the liver, causing allograft failure and possibly death. (4),(7),(8) It is therefore important to document on preoperative imaging the size and location of portosystemic varices in any patient being evaluated for liver transplantation. We present a case in which intraoperative sonography showed a large SSRS that impaired hepatic portal inflow after transplantation, ultimately resulting in the patient's death.