Exploring the Exciting Possibilities of Visual ChatGPT in Pressure Injury Care: Time for Change?

Abstract

INTRODUCTION Pressure injuries (PIs) remain a significant global public health issue, substantially diminishing the quality of life for patients and their families while escalating healthcare expenses.1,2 Despite many PIs being preventable or minimizable with the proper care and preventive measures, they continue to threaten patient safety, thus necessitating innovative applications of evidence-based interventions and nursing care strategies. In recent times, the healthcare sector has witnessed groundbreaking advancements with the introduction of artificial intelligence (AI). Systems like the Generative Pre-trained Transformer (GPT) series and Google Med-PaLM 2 are at the forefront of this revolution, showcasing their efficacy in improving patient engagement, enhancing diagnostic accuracy, and formulating person-centered treatment plans.3–5 Further, healthcare chatbots have gained prominence in this evolving landscape. For example, Ada Health, an AI-powered symptom checker and healthcare application, interacts with patients conversationally, collecting symptom-related data. This information is then leveraged to offer probable diagnoses and subsequent recommended actions (https://www.ada.com). Similarly, Buoy serves as an AI-assisted symptom checker, guiding patients toward suitable care pathways (https://www.buoyhealth.com). Beyond these examples, there are numerous chatbots operating in the healthcare domain, each contributing to varied facets of patient care and engagement. Although the benefits are apparent, the implementation of such tools warrants a careful approach. Before a large-scale rollout, pilot programs are imperative to assess potential limitations. Ensuring diverse and representative training data can help AI systems better understand varied patient demographics and conditions. Moreover, having a mechanism for human oversight can provide an additional layer of safety, ensuring that the recommendations align with current medical standards. Seamless integration with existing healthcare systems and infrastructures, coupled with a strong ethical framework that prioritizes patient data privacy and transparency, will further solidify the role of AI chatbots in the healthcare landscape. Over the past two decades, AI and decision-support systems have been pivotal in identifying at-risk patients, facilitating the staging of PIs, and enhancing nursing care and education. Nevertheless, it is crucial to recognize both the advancements and the present gaps in this domain. Toffaha et al’s6 comprehensive review illuminates the strides taken in employing AI for PI prediction and emphasizes the need for continuous improvement in AI systems for optimal predictive accuracy and patient care outcomes. Those authors also underline the imperative of addressing remaining gaps when applying AI in healthcare contexts. Recent advancements, especially in the domain of large language models, has led to the creation of visual AI chatbots, such as the Large Language and Visual Assistant (LLaVA) interface (accessible at https://llava.hliu.cc for the demo version and compatible with Microsoft Edge). By harnessing the capabilities of GPT-4 coupled with visual instruction models,7 the LLaVA interface emerges as a potentially transformative solution in PI management because it has the potential to bolster wound care nurses’ clinical decisions. However, to realize this potential, it is crucial that professionals across disciplines from engineering to healthcare work in tandem to optimize both the accuracy and the data safety of the LLaVA system. Considering the current limitations of visual ChatGPT applications in healthcare,8,9 could highly effective visual AI technology, such as LLaVA, help facilitate wound care nurses’ clinical decisions and improve patient outcomes? How could LLaVA be used in PI care? Interfaces such as LLaVA may serve as a valuable complement to wound care nurses in PI management, contributing to more accurate staging, better-informed treatment decisions, and better patient care. However, to ensure safe and efficient application of the LLaVA technology in clinical practice, it is essential to address the potential limitations and risks associated with its use.10 Thus, a collaborative effort between engineers and healthcare professionals is essential to guarantee both the precision of the LLaVA system and the safeguarding of patient data. In the subsequent sections, the authors outline their expectations for how the LLaVA interface may contribute to PI improvements, as well as limitations, risks, and challenges associated with its use. POTENTIAL CONTRIBUTIONS OF THE LLaVA INTERFACE TO PI IMPROVMENT Accurate staging of PIs is a crucial step in their management. The unique visual interface of LLaVA may aid in the accurate staging of PIs by analyzing PI images and responding to prompts.7 Upon uploading images of PIs at various stages, LLaVA assists in their classification, highlighting how AI technology can contribute to the better understanding of PIs. The inherent capacity of AI for continuous learning enables LLaVA to improve its capabilities through exposure to images of PIs at different stages and at different anatomic regions of the body, derived from actual patient scenarios. In this way, LLaVA could play a critical role in decision-making related to PI classification for wound care nurses and serve as an instructional tool for nursing students learning about PI. Providing Person-Centered Care Plans The LLaVA interface presents a unique opportunity for personalizing care plans, which could be instrumental in enhancing patient engagement.11 By leveraging LLaVA’s capabilities, healthcare professionals can provide visual insights and explanations customized to each patient’s specific PI condition. This visual feedback, combined with AI-driven recommendations, can offer patients a clearer understanding of their condition, its severity, and the required interventions. When patients see a care plan customized to their unique needs, it gives them a clearer vision of their health journey. They can understand not just the “what” but also the “why” behind each treatment step. This clarity can foster trust in the process and the healthcare provider. Moreover, the sense of ownership that comes from personalized care plans can play a crucial role in follow-up adherence. When a patient feels that a plan is crafted specifically for them, they recognize that they are not just following generic advice but a path specifically designed for their best outcome. This personal connection to the plan can make them more motivated to follow through with all the necessary steps, attend scheduled follow-up appointments, and actively participate in their own care. It is a psychological nuance: people are generally more committed to actions or plans that they feel a personal connection to, as opposed to generic or one-size-fits-all approaches.12 When patients perceive their treatment as being uniquely theirs, they inherently understand its importance and are more likely to stay engaged, leading to better adherence and, ultimately, better health outcomes. Enhanced Communication between Healthcare Providers and Patients The LLaVA interface also may enhance communication between healthcare providers and patients, serving as a valuable adjunct in patient engagement and education about wound severity. The visual capabilities of LLaVA can enrich communication by providing explicit visual representations of wounds; however, it should be acknowledged as an auxiliary tool for PI staging/management, rather than a standalone solution. By integrating visual representation with AI’s responsive capability to wound-related queries, LLaVA fosters a more interactive and transparent communication process. Patients may find it easier to understand the nature and severity of their PIs when the information is presented visually. Although the LLaVA system offers the prospect of crafting personalized care plans, it is imperative to navigate this technology with utmost consideration for the psychological and emotional impact it may have on patients. A potential challenge presents itself in the form of patient trauma when visual representations, particularly of severe conditions such as stage 3 PIs, are used in care communication. Indeed, showcasing images of PIs to patients might be traumatic. Consequently, although integrating the LLaVA system into patient care offers the potential for more individualized care plans, the approach in presenting and using this visual information must be tactfully managed. This involves crafting treatment plans customized to each patient’s unique needs while concurrently safeguarding their mental and emotional well-being, particularly when using visual aids or information derived from LLaVA in care planning and communication. Improving Patient Outcomes Applying LLaVA in PI care can lead to better patient outcomes. Accurate staging and early diagnosis of PIs enables timely and appropriate interventions, reducing complications and promoting wound healing. This technological integration could increase the quality of nursing care, leading to an improvement in patient care outcomes. Further, with LLaVA’s ability to analyze a large volume of data, predicting patient follow-up progress and adjusting treatment plans become more efficient and precisely. LIMITATIONS, RISKS, AND CHALLENGES Despite its potential benefits, the integration of LLaVA into clinical nursing practices must consider its limitations, risks, and challenges, as outlined below. Ensuring that AI-Generated Information is Accurate and Trustworthy Given that LLaVA is still under development, it often provides inaccurate detections and information. To safeguard against potential misinformation to patients and prevent clinical decisions that may be detrimental, it is imperative that wound care nurses employ their specialized knowledge and skills to validate and confirm the accuracy of AI-derived insights. Importantly, AI should not replace the decision-making of wound care nurses but facilitate and support their work. If wound care nurses make clinical decisions solely based on AI, it could negatively impact their decision-making skills. The lack of clinical research related to LLaVA should also be taken into account. Protecting Privacy and Data Security Privacy and data security are crucial, particularly when sharing patient data and PI images.13 When using LLaVA, healthcare professionals are responsible for ensuring that the necessary measures are in place to protect patient information. To maintain data privacy, patient data must be encrypted—a process that transforms readable data into a coded version. In addition, healthcare professionals should adopt strict data-handling protocols and controls in line with established guidelines and their local AI regulations.14 The terms of use for LLaVA state, “By using this service, users are required to agree to the following terms: The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content.” This caveat underscores the inherent risks of using a platform such as LLaVA in a clinical setting, especially concerning privacy and data security when sharing patient data and PI images. Given these constraints, there is minimal assurance healthcare professionals can rely upon to ensure protection of sensitive patient information when utilizing LLaVA. However, it should not be forgotten that the protective measures which healthcare professionals can institute independently are inherently limited. Therefore, for the protection of patient data, it is paramount for healthcare professionals to consider adopting AI solutions in personalized software that is compliant with established data regulations, such as the Health Insurance Portability and Accountability Act in the US and the General Data Protection Regulation in Europe, rather than resorting to open-source solutions, which may not offer the same level of data protection. Addressing Potential Biases in AI Algorithms The LLaVA interface learns from the data on which it is trained, meaning any bias in this data can influence the visual assistant’s decisions.15 For instance, a LLaVA system trained predominantly on images of PIs in light-skinned individuals might be less accurate in assessing PIs in individuals with darker skin tones. To ensure that the LLaVA interface works fairly and effectively across diverse patient populations, both clinicians and researchers need to follow a multistep process. First, the LLaVA should be trained using a diverse range of PI images from a broad spectrum of patients. This variety broadens the learning experience of the visual assistant and provides a more comprehensive understanding of PI characteristics. Second, the performance of the LLaVA must be continuously monitored and assessed by a combination of clinicians, AI researchers, and data scientists. Clinicians play a crucial role in identifying biases or inaccuracies in clinical settings, and AI researchers and data scientists should analyze and refine the algorithms based on performance data. If biases are uncovered, it might be necessary to adjust how the LLaVA is learning from the data. Adjustments to learning can be accomplished in different ways, such as changing the training data or the framework of the LLaVA model architecture. The LLaVA also needs to clearly explain how it makes decisions so that healthcare providers can understand and trust it. Last, even with the visual assistant, healthcare providers still need to check its recommendations using their own knowledge and experience. Integrating LLaVA with Existing Healthcare Systems Although the literature suggests that nurses and physicians prefer technology-based education in PI training and the use of wound images is beneficial, integrating the LLaVA interface into current healthcare systems is a complex task.16 Consequently, preliminary pilot studies, evaluations, and necessary improvements should be planned. Training and encouragement for healthcare professionals, especially wound care nurses, to take an active role in this integration are also essential. CONCLUSIONS Using visual AI chatbots such as LLaVA for PI management offers numerous advantages, but also presents challenges that must be considered. As AI continues to evolve, it is crucial to adapt these cutting-edge technologies fairly, effectively, and responsibly. This commitment is not merely a requirement: it represents a dedication to advancing the future of nursing care and education. Moreover, ongoing dialogue, coupled with the sharing of experiences, knowledge, and challenges, is essential to create a roadmap that ensures AI serves as a boon, not a bane, in the healthcare field.17

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