Artificial intelligence: a new field of knowledge for nephrologists?

Abstract

Artificial Intelligence (AI) is a science that involves creating machines that can imitate human intelligence and learn. AI is ubiquitous in our daily lives, from search engines like Google to home assistants like Alexa and, more recently, OpenAI with its chatbot. AI can improve clinical care and research, but its use requires a solid understanding of its fundamentals, the promises and perils of algorithmic fairness, the barriers and solutions to its clinical implementation, and the pathways to developing an AI-competent workforce. The potential of AI in the field of nephrology is vast, particularly in the areas of diagnosis, treatment, and prediction. One of the most significant advantages of AI is the ability to improve diagnostic accuracy. Machine learning algorithms can be trained to recognize patterns in patient data, including lab results, imaging, and medical history, in order to identify early signs of kidney disease and thereby allow timely diagnoses and prompt initiation of treatment plans that can improve outcomes for patients. In short, AI holds the promise of advancing personalized medicine to new levels. While AI has tremendous potential, there are also significant challenges to its implementation, including data access and quality, data privacy and security, bias, trustworthiness, computing power, AI integration, and legal issues. The European Commission's proposed regulatory framework for AI technology will play a significant role in ensuring the safe and ethical implementation of these technologies in the healthcare industry. Training nephrologists in the fundamentals of AI is imperative because traditionally, decision-making pertaining to the diagnosis, prognosis, and treatment of renal patients has relied on ingrained practices, whereas AI serves as a powerful tool for swiftly and confidently synthesizing this information.

Advances in Human-Centered Care to Address Contemporary Unmet Needs in Chronic Dialysis

“…Key issues that slow the adoption of AI in medicine include concerns with data privacy and security; issues regarding maintaining transparency and accountability of decision-making; and the formidable task of attaining the requisite computing power for timely processing of healthcare data. 120 …”

The leading global health challenges in the artificial intelligence era

“…If we discuss kidney disease progression as a case illustration, AI has its application in pre/post-diagnosis, which can ultimately lead to improved outcomes in a timely and accurate manner ( 20 ). During the analysis phase of patient data, AI can identify the early signs of the diseases from the lab results, medical history, and images ( 21 , 22 ), it can also help in the diagnosis of kidney disease from a kidney biopsy through deep learning-based approaches ( 23 ), and to improve outcome and early detection of other comorbidity in renal patients ( 24 ). The flowing part discusses AI’s applications in the major of NCDs.…”