Artificial intelligence (AI) is changing the face of modern medicine. In fields like cardiology and oncology, it’s already helping doctors detect disease earlier, personalize treatments, and improve outcomes. Now, AI is making its way into nephrology, the world of kidney care, bringing with it new hope for patients living with chronic kidney disease (CKD) and kidney failure.

Experts say the technology is still in its early stages, but research is accelerating quickly. From predicting acute kidney injury (AKI) to improving dialysis and transplant outcomes, AI is poised to make care more efficient, more accurate, and more patient-centered.*

How AI Is Helping Kidney Doctors Work Smarter

Nephrologists spend a large part of their day searching through patient records, test results, and notes. AI can help by streamlining that process.

Dr. Fahim Rahim, a nephrologist and cofounder of the start-up Nephrolytics, helped develop Saya, an AI platform that pulls together 20 years of patient data. It transcribes patient visits, flags missing information, and automates documentation, freeing up time for doctors to focus on patients rather than paperwork.

Meanwhile, the Renal Research Institute, part of Fresenius Medical Care, has created an AI-powered chatbot that helps dietitians build customized meal plans based on patients’ health needs and food preferences. In the future, similar chatbots could answer patient questions, provide reminders about dialysis and medication, and alert care teams to concerning symptoms.

“AI can help bridge gaps in communication and give clinicians back valuable time,” said Dr. Rahim.

Detecting Kidney Problems Earlier

AI is showing promise in detecting acute kidney injury (AKI), a sudden decline in kidney function, before it becomes life-threatening.

Dr. Prabhat Singh, a nephrologist in Texas, explained that traditional methods like blood and urine tests often catch AKI after the kidneys are already damaged. New AI models can analyze patterns in hospital data to spot warning signs sooner, giving doctors a chance to intervene earlier.

Some studies show AI can predict AKI more accurately than older methods, even identifying which patients are at risk after surgery or during serious infections like sepsis. However, Dr. Singh cautions that results vary between studies, and more testing is needed before these tools become routine in hospitals.

Catching CKD Earlier and Personalizing Risk

One of AI’s biggest strengths is pattern recognition—spotting problems hidden in complex data. That’s especially useful for chronic kidney disease, which often progresses silently for years.

Dr. Debargha Basuli from East Carolina University said deep learning models trained on electrocardiogram (ECG) or retinal images can now detect CKD with impressive accuracy. “The eye’s blood vessels can mirror what’s happening in the kidney,” he explained.

AI is also being used for risk stratification, identifying which CKD patients are most likely to experience rapid decline. A major example is KidneyIntelX, a test developed at Mount Sinai Health System. It combines blood biomarkers with a machine-learning algorithm to predict disease progression in people with diabetes and early CKD.

The test is now covered by Medicare, and nearly 600 clinicians have started using it to guide treatment decisions. By pinpointing who needs the most aggressive care, tools like this can help doctors act earlier to preserve kidney function.

Improving Dialysis Decisions

Every dialysis session generates massive amounts of data, including information about fluid removal, blood pressure, lab results, and more. AI can analyze this data to help healthcare teams make better decisions.

At Fresenius clinics across the U.S. and Europe, AI algorithms run nightly to predict which patients are at risk of hospitalization from fluid overload or infection. Nurses and doctors are alerted so they can adjust medications or treatment plans the next day. Studies have linked their use to reduce hospitalizations. 

In Europe, a separate algorithm helps manage anemia treatments, automatically recommending dose adjustments for patients receiving dialysis. Clinicians always have the final say, but these tools have already been linked to fewer hospitalizations and better health outcomes.

AI is also being explored to predict which patients might thrive on home dialysis and which may need to transition back to in-center care, offering more personalized and proactive management.

Supporting Safer Kidney Transplants

Transplant specialists are also testing AI to improve outcomes for both donors and recipients.

Dr. Lisiane Pruinelli, a transplant researcher at the University of Florida, said teams are experimenting with “virtual biopsy” models that can predict the condition of donor kidneys without needing an invasive sample. One such model analyzed 14,000 donor records and accurately forecasted kidney health based on common data like age, weight, and lab results.

AI models are also helping forecast graft survival, how long a transplanted kidney will last. In the UK, one AI system outperformed existing prediction tools. Another model, called iBox, already approved in Europe, uses lab results and biopsy findings to predict graft success up to seven years post-transplant.

Researchers are even exploring whether AI can tailor immunosuppressant medications based on the genetics of each donor-recipient pair, a step toward true precision medicine in transplantation.

Discovering New Treatments

AI isn’t just improving existing care—it’s helping scientists discover new drugs.

At the University of Pennsylvania, researchers developed an AI-based kidney cell atlas called SISKA, which maps over a million kidney cells from humans, mice, and rats. This database helps scientists understand which cells and genes are affected by different kidney diseases.

They also built a tool called CellSpectra, which compares an individual’s kidney cells to the reference atlas to identify which genes are malfunctioning. This could someday help doctors match patients to targeted therapies, or even develop new ones.

While these tools aren’t yet available in clinics, they show how AI may one day make kidney care more personalized than ever.

Challenges Ahead

Despite the excitement, experts caution that AI isn’t a cure-all.

Dr. Singh warned that bias in training data can lead to inaccurate predictions, especially if datasets don’t represent diverse populations. Poor data quality, inconsistent record-keeping, and “black box” algorithms (which can’t easily explain their reasoning) are other major challenges.

Ethical questions are also front and center:

• Who’s responsible if an AI model makes a mistake, the developer or the doctor?
• How do we ensure patients’ private data stays secure?
• Should insurers use AI to approve or deny treatments?

Dr. Pruinelli emphasized the need for ongoing oversight:

“We cannot implement these AI models and let them go. We have to monitor them day to day to detect when they make a mistake and be ready to fix it.”

What This Means for You

While most AI tools are still in research, they represent an exciting step forward in kidney care. In the future, AI could help your doctor:

• Catch kidney problems earlier
• Personalize your treatment plan
• Prevent hospitalizations
• Predict which therapies will work best for you

For now, staying informed and engaged in your own care remains the most important part of managing CKD. AI may someday be another tool in your healthcare team’s toolkit, but your voice, questions, and choices still matter most.

* Medscape (October 10, 2025). “How AI Is Transforming Kidney Care. medscape.com

To ensure that we always provide you with high-quality, reliable information, Responsum Health closely vets all sources. We do not, however, endorse or recommend any specific providers, treatments, or products, and the use of a given source does not imply an endorsement of any provider, treatment, medication, procedure, or device discussed within.