Exploring the Potential of Chatbots in Critical Care Nephrology

Abstract

The exponential growth of artificial intelligence (AI) has allowed for its integration into multiple sectors, including, notably, healthcare. Chatbots have emerged as a pivotal resource for improving patient outcomes and assisting healthcare practitioners through various AI-based technologies. In critical care, kidney-related conditions play a significant role in determining patient outcomes. This article examines the potential for integrating chatbots into the workflows of critical care nephrology to optimize patient care. We detail their specific applications in critical care nephrology, such as managing acute kidney injury, alert systems, and continuous renal replacement therapy (CRRT); facilitating discussions around palliative care; and bolstering collaboration within a multidisciplinary team. Chatbots have the potential to augment real-time data availability, evaluate renal health, identify potential risk factors, build predictive models, and monitor patient progress. Moreover, they provide a platform for enhancing communication and education for both patients and healthcare providers, paving the way for enriched knowledge and honed professional skills. However, it is vital to recognize the inherent challenges and limitations when using chatbots in this domain. Here, we provide an in-depth exploration of the concerns tied to chatbots’ accuracy, dependability, data protection and security, transparency, potential algorithmic biases, and ethical implications in critical care nephrology. While human discernment and intervention are indispensable, especially in complex medical scenarios or intricate situations, the sustained advancements in AI signal that the integration of precision-engineered chatbot algorithms within critical care nephrology has considerable potential to elevate patient care and pivotal outcome metrics in the future.

Personalized Medicine Transformed: ChatGPT’s Contribution to Continuous Renal Replacement Therapy Alarm Management in Intensive Care Units

“…Our study explored whether large language models (LLMs) like ChatGPT can assist with CRRT machine alarms [ 20 ]. We aimed to evaluate ChatGPT’s ability to generalize across various CRRT devices by testing it with a range of sample alarms and issues.…”

“…In addition, future research should look beyond just OpenAI’s GPT-4 to examine a wider range of LLMs. This should include open-source options such as Falcon, LLaMA, and Guanaco-65B, as well as proprietary models like Claude and PaLM 2 [ 20 ]. Each model has unique strengths, from Falcon’s language breadth and Guanaco-65B’s memory efficiency to Claude’s ethical foundations and PaLM 2’s advanced reasoning.…”

“…Applications within healthcare have included automated diagnosis, optimized imaging interpretation, personalized treatment plans and improved clinical workflows. However, the integration of AI in the niche domain of CRRT management remains limited and under-investigated [ 20 ]. Currently, there lies a significant gap in the literature when it comes to leveraging NLP models for optimizing CRRT practices, alarm responses, and patient safety protocols.…”

“…Currently, there lies a significant gap in the literature when it comes to leveraging NLP models for optimizing CRRT practices, alarm responses, and patient safety protocols. While few studies have commented generally on AI applications in nephrology, empirical evidence evaluating model performance on deciphering CRRT alarms is lacking [ 20 ]. Most AI research has, understandably, emphasized more prevalent specialties, whereas CRRT management represents an underexplored frontier [ 20 ].…”

“…While few studies have commented generally on AI applications in nephrology, empirical evidence evaluating model performance on deciphering CRRT alarms is lacking [ 20 ]. Most AI research has, understandably, emphasized more prevalent specialties, whereas CRRT management represents an underexplored frontier [ 20 ].…”