With the demand for human donor organs desperately outstripping supply, scientists are working to see if genetically edited pig organs can bridge the gap. Leonardo Riella, medical director for kidney transplantation at Massachusetts General Hospital, checks on Tim Andrews after his pig kidney transplant. Surgeons at Massachusetts General Hospital have removed a genetically engineered pig kidney from a 67-year-old New Hampshire man after a period of decreasing kidney function, the hospital confirmed to WIRED in a statement. The organ functioned for nearly nine months, longer than previous pig organ transplants, before it was removed on October 23. Tim Andrews received the pig kidney on January 25 after being on dialysis for more than two years due to end-stage kidney disease.

Health Diseases Donor organ's blood type altered for the first time Scientists removed the blood's antigens to make a kidney the universal type-O. Breakthroughs, discoveries, and DIY tips sent every weekday. In a world first, researchers at the University of British Columbia (UBC) in Canada successfully transplanted a human donor kidney that they artificially swapped from someone with type-A blood to the universal type-O. The breakthrough may pave the way for the creation of a universal donor blood supply, as well as the ability to pull off similar results with other vital organs. The riskiest and often most difficult part of an organ transplant procedure is the distinct possibility that a patients' body will reject the organ itself .

In a warming world, migratory birds face many existential threats. Scientists rely on a combination of methods to track the timing and location of their migrations, but each has shortcomings. And there's another problem: Most birds migrate at night, when it's more difficult to identify them visually and while most birders are in bed. For over a century, acoustic monitoring has hovered tantalizingly out of reach as a method that would solve ornithologists' woes. Now, finally, machine-learning tools are unlocking a treasure trove of acoustic data for ecologists.

Tuomas Sandholm is the winner of the 2023 AAAI Award for Artificial Intelligence for the Benefit of Humanity. This award recognizes positive impacts of artificial intelligence to protect, enhance, and improve human life in meaningful ways. Tuomas delivered an invited talk at the AAAI Conference on Artificial Intelligence, during which he spoke about the work that won him the award – using algorithms for organ exchanges. Kidney disease is becoming more prevalent in the world, and, in the USA alone, over 90,000 people are waiting for a kidney transplant. This waiting list keeps growing year on year.

Chronic kidney disease (CKD) represents a slowly progressive disorder that can eventually require renal replacement therapy (RRT) including dialysis or renal transplantation. Early identification of patients who will require RRT (as much as 1 year in advance) improves patient outcomes, for example by allowing higher-quality vascular access for dialysis. Therefore, early recognition of the need for RRT by care teams is key to successfully managing the disease. Unfortunately, there is currently no commonly used predictive tool for RRT initiation. In this work, we present a machine learning model that dynamically identifies CKD patients at risk of requiring RRT up to one year in advance using only claims data. To evaluate the model, we studied approximately 3 million Medicare beneficiaries for which we made over 8 million predictions. We showed that the model can identify at risk patients with over 90% sensitivity and specificity. Although additional work is required before this approach is ready for clinical use, this study provides a basis for a screening tool to identify patients at risk within a time window that enables early proactive interventions intended to improve RRT outcomes.

From self-driving cars to virtual travel agents, artificial intelligence has quickly transformed the landscape for nearly every industry. The technology is also employed in healthcare to help with clinical decision support, imaging and triage. However, using AI in a healthcare setting poses a unique set of ethical and logistical challenges. MobiHealthNews asked health tech vet Muhammad Babur, a program manager at the Mayo Clinic, about the potential challenges and ethics behind using AI in healthcare ahead of his upcoming discussion at HIMSS22. MobiHealthNews: What are some of the challenges to using AI in healthcare? Babur: The challenges that we face in healthcare are unique and more consequential.

Renal transplantation is widely regarded as the best treatment for patients with end-stage kidney disease. Over the past 15 years, demand in the UK for kidney transplants has been rising, resulting in more elderly deceased donors being considered. The problem with elderly donors is that kidney function deteriorates with age. Kidney transplants from elderly donors are associated with higher risks of early failure. Early failure of a kidney graft is a disastrous outcome for the recipient.

Artificial intelligence has stepped in to help complete the medication information in the medical record for patients who can't remember what drugs they are taking, let alone the dose. This is most of us, according to Rebecca Sulfridge, a clinical pharmacist and emergency specialist at Covenant HealthCare in Michigan. Most patients have gaps in their medication history. Technicians hear, "'I take three pills. One is a little white one. One is a pink one,'" or "'I know I take two blood pressure meds and one to sleep at night,'" in face-to-face talks with patients, according to Sulfridge.

Kidney transplantation is the best treatment for end-stage renal failure patients. The predominant method used for kidney quality assessment is the Cox regression-based, kidney donor risk index. A machine learning method may provide improved prediction of transplant outcomes and help decision-making. A popular tree-based machine learning method, random forest, was trained and evaluated with the same data originally used to develop the risk index (70,242 observations from 1995-2005). The random forest successfully predicted an additional 2,148 transplants than the risk index with equal type II error rates of 10%. Predicted results were analyzed with follow-up survival outcomes up to 240 months after transplant using Kaplan-Meier analysis and confirmed that the random forest performed significantly better than the risk index (p<0.05). The random forest predicted significantly more successful and longer-surviving transplants than the risk index. Random forests and other machine learning models may improve transplant decisions.

AI algorithms increasingly make decisions that impact entire groups of humans. Since humans tend to hold varying and even conflicting preferences, AI algorithms responsible for making decisions on behalf of such groups encounter the problem of preference aggregation: combining inconsistent and sometimes contradictory individual preferences into a representative aggregate. In this paper, we address this problem in a real-world public health context: kidney exchange. The algorithms that allocate kidneys from living donors to patients needing transplants in kidney exchange matching markets should prioritize patients in a way that aligns with the values of the community they serve, but allocation preferences vary widely across individuals. In this paper, we propose, implement and evaluate a methodology for prioritizing patients based on such heterogeneous moral preferences. Instead of selecting a single static set of patient weights, we learn a distribution over preference functions based on human subject responses to allocation dilemmas, then sample from this distribution to dynamically determine patient weights during matching. We find that this methodology increases the average rank of matched patients in the sampled preference ordering, indicating better satisfaction of group preferences. We hope that this work will suggest a roadmap for future automated moral decision making on behalf of heterogeneous groups.