Large Language and Vision Assistant in Dermatology: A Game Changer or Just Hype?

The integration of artificial intelligence (AI) in healthcare, particularly in the field of dermatology, has experienced significant progress through the creation of advanced tools such as the Large Language and Vision Assistant (LLaVA). This comprehensive review examines whether LLaVA represents a significant breakthrough or merely a passing trend in dermatological practice. By incorporating both language and visual analysis capabilities, LLaVA aims to support enhanced diagnostic accuracy, patient engagement, and customized treatment planning, as evidenced by current research and case studies. However, its practical utility in a clinical setting remains a subject of debate. We explore the visual assistant chatbot's potential in improving diagnostic precision, especially in analyzing skin lesions and conditions that are visually complex. The tool's capacity to process and interpret dermatological images using advanced algorithms could aid clinicians in early detection and management of skin diseases. Furthermore, LLaVA's interactive nature potentially improves patient education and adherence to treatment protocols. Despite these advantages, there are noteworthy limitations and risks. The accuracy of LLaVA in handling atypical or rare dermatological cases is an area of concern. The tool's reliance on existing medical data raises questions about bias and the generalizability of its findings. Additionally, ethical considerations, such as patient data privacy and the potential for overreliance on AI in clinical decision-making, are critical issues that need addressing. This article aims to provide dermatologists with a comprehensive understanding of large language and visual assistant's capabilities and limitations. We discuss practical guidelines for its integration into research and clinical educational augmentation, ensuring that dermatologists can make informed decisions about employing this technology for the enhancement of patient care and treatment outcomes. The question remains: is LLaVA a game changer in dermatology, or is it just hype? This review endeavours to answer this, establishing a foundation for knowledgeable and efficient application of visual AI chatbots in dermatology practices.

Assessing the virtual reality perspectives and self-directed learning skills of nursing students: A machine learning-enhanced approach.

AIM: This study aims to investigate nursing students' perspectives on virtual reality technologies and their self-directed learning skills, specifically focusing on how these variables interact and influence each other in the context of nursing education. We also discern potential disparities in these skills based on descriptive characteristics, using both traditional statistical and advanced machine learning approaches for a comprehensive analysis. BACKGROUND: Rapid developments in technology, particularly during the Covid-19 pandemic, have brought virtual reality technologies to the forefront of nursing education. However, there is a gap in understanding how nursing students' perceptions of these technological relate to their development of self-directed learning skills. DESIGN: A descriptive and cross-sectional study design is employed to both quantify nursing students' perspectives on virtual reality in their education and assess their self-directed learning skills. This approach integrates traditional statistical methods with advanced machine learning techniques, with the intention of offering a comprehensive and nuanced analysis to inform future teaching strategies in nursing. METHODS: The study used a blend of survey scales and a tree-based machine learning model to measure and analyze nursing students' views, attitudes and self-directed learning levels. This dual approach allows for a more detailed assessment of the factors influencing self-directed learning abilities. Traditional statistical techniques were also applied to assess the reliability of the machine learning findings. RESULTS: Findings reveal that nursing students generally held positive views towards virtual reality technologies and exhibited a high level of self-directed learning skills. Notable differences in self-directed learning skills were influenced by gender on the overall scale (p <0.001), with male students scoring higher than their female counterparts in both specific sub-dimensions and on the overall scale, but not by academic year. The machine learning analysis provided deeper insights into these variations, highlighting subtle distinctions in student demographics that traditional statistical methods did not fully capture. CONCLUSIONS: The study offers valuable insights into interconnected nature of nursing students' views on virtual reality technologies and their self-directed learning skills. The results support the integration of virtual reality in nursing curriculum programs and underscore the importance of customizing teaching strategies based on insights gained from machine learning analyses. This approach has the potential to substantially improve both the learning experience and the overall quality of nursing education.

PPSW-SHAP: Towards Interpretable Cell Classification Using Tree-Based SHAP Image Decomposition and Restoration for High-Throughput Bright-Field Imaging.

Advancements in high-throughput microscopy imaging have transformed cell analytics, enabling functionally relevant, rapid, and in-depth bioanalytics with Artificial Intelligence (AI) as a powerful driving force in cell therapy (CT) manufacturing. High-content microscopy screening often suffers from systematic noise, such as uneven illumination or vignetting artifacts, which can result in false-negative findings in AI models. Traditionally, AI models have been expected to learn to deal with these artifacts, but success in an inductive framework depends on sufficient training examples. To address this challenge, we propose a two-fold approach: (1) reducing noise through an image decomposition and restoration technique called the Periodic Plus Smooth Wavelet transform (PPSW) and (2) developing an interpretable machine learning (ML) platform using tree-based Shapley Additive exPlanations (SHAP) to enhance end-user understanding. By correcting artifacts during pre-processing, we lower the inductive learning load on the AI and improve end-user acceptance through a more interpretable heuristic approach to problem solving. Using a dataset of human Mesenchymal Stem Cells (MSCs) cultured under diverse density and media environment conditions, we demonstrate supervised clustering with mean SHAP values, derived from the 'DFT Modulus' applied to the decomposition of bright-field images, in the trained tree-based ML model. Our innovative ML framework offers end-to-end interpretability, leading to improved precision in cell characterization during CT manufacturing.

Artificial Intelligence Chatbots in Allergy and Immunology Practice: Where Have We Been and Where Are We Going?

Artificial intelligence (AI) is rapidly becoming a valuable tool in healthcare, providing clinicians with a new AI lens perspective for patient care, diagnosis, and treatment. This article explores the potential applications, benefits, and challenges of AI chatbots in clinical settings, with a particular emphasis on ChatGPT 4.0 (OpenAI - Chat generative pretrained transformer 4.0), especially in the field of allergy and immunology. AI chatbots have shown considerable promise in various medical domains, including radiology and dermatology, by improving patient engagement, diagnostic accuracy, and personalized treatment plans. ChatGPT 4.0, developed by OpenAI, is good at understanding and replying to prompts in a way that makes sense. However, it is critical to address the potential biases, data privacy issues, ethical considerations, and the need for verification of AI-generated findings. When used responsibly, AI chatbots can significantly enhance clinical practice in allergy and immunology. However, there are still challenges in using this technology that require ongoing research and collaboration between AI developers and medical specialists. To this end, the ChatGPT 4.0 platform has the potential to enhance patient engagement, improve diagnostic accuracy, and provide personalized treatment plans in allergy and immunology practice. However, limitations and risks must be addressed to ensure their safe and effective use in clinical practice.

Thermo-SPT: A new skin prick test evaluation framework based on low-cost, portable smartphone thermography.

BACKGROUND: Although the skin prick test (SPT) is a reliable procedure to confirm IgE-dependent allergic sensitization in patients, the interpretation of the test is still performed manually, resulting in an error-prone procedure for the diagnosis of allergic diseases. OBJECTIVE: To design and implement an innovative SPT evaluation framework using a low-cost, portable smartphone thermography, named Thermo-SPT, to significantly improve the accuracy and reliability of SPT outcomes. METHODS: Thermographical images were captured every 60 s for a duration of 0 to 15 min using the FLIR One app, and then analysed with the FLIR Tool® . The definition of 'Skin Sensitization Region' area was introduced to analyse the time-lapse thermal changes in skin reactions over several time periods during the SPT. The Allergic Sensitization Index (ASI) and Min-Max Scaler Index (MMS) formulae were also developed to optimize the identification of the peak allergic response time point through the thermal assessment (TA) of allergic rhinitis patients. RESULTS: In these experimental trials, a statistically significant increase in temperature was detected from the fifth minute of TA for all tested aeroallergens (all p values < .001 ). An increase was observed in the number of false-positive cases, where patients with clinical symptoms not consistent with SPT were evaluated as positive on TA assessment, specifically for patients diagnosed with Phleum pratense and Dermatophagoides pteronyssinus. Our proposed technique, the MMS, has demonstrated improved accuracy in identifying P. pratense and D. pteronyssinus compared with other SPT evaluation metrics, specifically starting from the fifth minute. For patients diagnosed with Cat epithelium, although not statistically significant initially, an increasing trend was determined in the results at the 15 min (ΔT (T15 - T0 ), p = .07 ; ASIT15 , p < .001 ). CONCLUSIONS: This proposed SPT evaluation framework utilizing a low-cost, smartphone-based thermographical imaging technique can enhance the interpretability of allergic responses during the SPT, potentially reducing the need for extensive manual interpretation experience as standard SPTs.

A Novel Fragmentation Sensitivity Index Determines the Susceptibility of Red Blood Cells to Mechanical Trauma.

Supraphysiological shear stresses (SSs) induce irreversible impairments of red blood cell (RBC) deformability, overstretching of RBC membrane, or fragmentation of RBCs that causes free hemoglobin to be released into plasma, which may lead to anemia. The magnitude and exposure tisme of the SSs are two critical parameters that determine the hemolytic threshold of a healthy RBC. However, impairments in the membrane stability of damaged cells reduce the hemolytic threshold and increase the susceptibility of the cell membrane to supraphysiological SSs, leading to cell fragmentation. The severity of the RBC fragmentation as a response to the mechanical damage and the critical SS levels causing fragmentation are not previously defined. In this study, we investigated the RBC mechanical damage in oxidative stress (OS) and metabolic depletion (MD) models by applying supraphysiological SSs up to 100 Pa by an ektacytometer (LORRCA MaxSis) and then assessed RBC deformability. Next, we examined hemolysis and measured RBC volume and count by Multisizer 3 Coulter Counter to evaluate RBC fragmentation. RBC deformability was significantly impaired in the range of 20-50 Pa in OS compared with healthy controls (p < 0.05). Hemolysis was detected at 90-100 Pa SS levels in MD and all applied SS levels in OS. Supraphysiological SSs increased RBC volume in both the damage models and the control group. The number of fragmented cells increased at 100 Pa SS in the control and MD and at all SS levels in OS, which was accompanied by hemolysis. Fragmentation sensitivity index increased at 50-100 Pa SS in the control, 100 Pa SS in MD, and at all SS levels in OS. Therefore, we propose RBC fragmentation as a novel sensitivity index for damaged RBCs experiencing a mechanical trauma before they undergo fragmentation. Our approach for the assessment of mechanical risk sensitivity by RBC fragmentation could facilitate the close monitoring of shear-mediated RBC response and provide an effective and accurate method for detecting RBC damage in mechanical circulatory assist devices used in routine clinical procedures.

Image-Based Flow Cytometry and Angle-Resolved Light Scattering to Define the Sickling Process.

Red blood cells (RBCs) from sickle cell patients exposed to a low oxygen tension reveal highly heterogeneous cell morphologies due to the polymerization of sickle hemoglobin (HbS). We show that angle-resolved light scattering approach with the use of image-based flow cytometry provides reliable quantitative data to define the change in morphology of large populations of RBCs from sickle cell patients when the cells are exposed for different times to low oxygen. We characterize the RBC morphological profile by means of a set of morphological and physical parameters, which includes cell shape, size, and orientation. These parameters define the cell as discocyte, sickle, elongated, as well as irregularly or abnormal RBC shaped cells, including echinocytes, holly-leaf, and granular structures. In contrast to microscopy, quick assessment of large numbers of cells provides statistically relevant information of the dynamic process of RBC sickling in time. The use of this approach facilitates the understanding of the processes that define the propensity of sickle blood samples to change their shape, and the ensuing vaso-occlusive events in the circulation of the patients. Moreover, it assists in the evaluation of treatments that include the use of anti-sickling agents, gene therapy-based hemoglobin modifications, as well as other approaches to improve the quality of life of sickle cell patients. © 2019 International Society for Advancement of Cytometry.