Patients have long sought health information online, and increasingly, they are turning to generative AI to answer their health-related queries. Given the high stakes of the medical domain, techniques like retrieval-augmented generation and citation grounding have been widely promoted as methods to reduce hallucinations and improve the accuracy of AI-generated responses and have been widely adopted into search engines. This paper argues that even when these methods produce literally accurate content drawn from source documents sans hallucinations, they can still be highly misleading. Patients may derive significantly different interpretations from AI-generated outputs than they would from reading the original source material, let alone consulting a knowledgeable clinician. Through a large-scale query analysis on topics including disputed diagnoses and procedure safety, we support our argument with quantitative and qualitative evidence of the suboptimal answers resulting from current systems. In particular, we highlight how these models tend to decontextualize facts, omit critical relevant sources, and reinforce patient misconceptions or biases. We propose a series of recommendations -- such as the incorporation of communication pragmatics and enhanced comprehension of source documents -- that could help mitigate these issues and extend beyond the medical domain.

Predicated on the increasing abundance of electronic health records, we investigate the problem of inferring individualized treatment effects using observational data. Stemming from the potential outcomes model, we propose a novel multitask learning framework in which factual and counterfactual outcomes are modeled as the outputs of a function in a vector-valued reproducing kernel Hilbert space (vvRKHS). We develop a nonparametric Bayesian method for learning the treatment effects using a multi-task Gaussian process (GP) with a linear coregionalization kernel as a prior over the vvRKHS. The Bayesian approach allows us to compute individualized measures of confidence in our estimates via pointwise credible intervals, which are crucial for realizing the full potential of precision medicine. The impact of selection bias is alleviated via a risk-based empirical Bayes method for adapting the multi-task GP prior, which jointly minimizes the empirical error in factual outcomes and the uncertainty in (unobserved) counterfactual outcomes. We conduct experiments on observational datasets for an interventional social program applied to premature infants, and a left ventricular assist device applied to cardiac patients wait-listed for a heart transplant. In both experiments, we show that our method significantly outperforms the state-of-the-art.

Animal-to-human transplant science took a major step forward this week after surgeons transplanted a kidney and a liver from pigs into humans. In Boston, a 62-year-old terminal patient received a genetically-altered kidney from a pig in a world first earlier this month. The new organ began to produce urine almost immediately, doctors at Mass General said, and the patient is stable and walking. Meanwhile, in China, a 50-year-old brain-dead man became the first to receive a genetically-engineered liver from a pig -- which was kept in his body for 10 days. Surgeons say the organ's color and texture appeared'normal' upon extraction and that it was even secreting bile -- a fluid aiding digestion.

Surgeon Abraham Shaked thinks he has probably carried out more than 2,500 liver transplants. But in December 2023, the team he oversees at the University of Pennsylvania did something he'd never tried before. Working on the body of a brain-dead man, they attached his veins to a refrigerator-size machine with a pig liver mounted in the middle of it. For three days, the man's blood passed into the machine, through the pig liver, and back into his body. This "extracorporeal," or outside-the-body, liver is designed to help people survive acute liver failure.

Larry Faucette, 58, pictured at left, was the second person to receive a genetically modified pig heart. Dr. Bartley Griffith, professor of surgery at the University of Maryland School of Medicine who performed the surgery, is shown at right. LIFE-SAVING EXPERIMENT – Surgeons give an update on a patient with end-stage cardiac disease a month after he received a genetically modified pig heart. BREAST CANCER BREAKTHROUGH – Experts claim artificial intelligence could predict a woman's risk years before a diagnosis is given. NOT-SO-SWEET DREAMS – When does snoring go from a common annoyance to a sign of a serious condition?

Organ transplant is the essential treatment method for some end-stage diseases, such as liver failure. Analyzing the post-transplant cause of death (CoD) after organ transplant provides a powerful tool for clinical decision making, including personalized treatment and organ allocation. However, traditional methods like Model for End-stage Liver Disease (MELD) score and conventional machine learning (ML) methods are limited in CoD analysis due to two major data and model-related challenges. To address this, we propose a novel framework called CoD-MTL leveraging multi-task learning to model the semantic relationships between various CoD prediction tasks jointly. Specifically, we develop a novel tree distillation strategy for multi-task learning, which combines the strength of both the tree model and multi-task learning. Experimental results are presented to show the precise and reliable CoD predictions of our framework. A case study is conducted to demonstrate the clinical importance of our method in the liver transplant. 1 Introduction Organ transplant is a crucial therapeutic option for individuals with end-stage diseases, e.g., kidney failure [1], liver failure [2], liver cancer [3], etc.

Kernel survival analysis models estimate individual survival distributions with the help of a kernel function, which measures the similarity between any two data points. Such a kernel function can be learned using deep kernel survival models. In this paper, we present a new deep kernel survival model called a survival kernet, which scales to large datasets in a manner that is amenable to model interpretation and also theoretical analysis. Specifically, the training data are partitioned into clusters based on a recently developed training set compression scheme for classification and regression called kernel netting that we extend to the survival analysis setting. At test time, each data point is represented as a weighted combination of these clusters, and each such cluster can be visualized. For a special case of survival kernets, we establish a finite-sample error bound on predicted survival distributions that is, up to a log factor, optimal. Whereas scalability at test time is achieved using the aforementioned kernel netting compression strategy, scalability during training is achieved by a warm-start procedure based on tree ensembles such as XGBoost and a heuristic approach to accelerating neural architecture search. On four standard survival analysis datasets of varying sizes (up to roughly 3 million data points), we show that survival kernets are highly competitive compared to various baselines tested in terms of time-dependent concordance index. Our code is available at: https://github.com/georgehc/survival-kernets

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A Spanish hospital carried out a lung transplant using a pioneering technique with a robot and a new access route that no longer requires separating the ribs and opening up the chest, experts said on Monday. Surgeons at Vall d'Hebron hospital in Barcelona used a four-arm robot dubbed "Da Vinci" to cut a small section of the patient's skin, fat and muscle to remove the damaged lung and insert a new one through an eight-centimetre incision in the lower part of the sternum, just above the diaphragm. The new procedure is less painful for the patient, they said, as the wound closes easily, and is safer than the traditional method which requires a 30-centimetre incision and a very delicate post-operative period.

Liver transplantation is a life-saving procedure for patients with end-stage liver disease. There are two main challenges in liver transplant: finding the best matching patient for a donor and ensuring transplant equity among different subpopulations. The current MELD scoring system evaluates a patient's mortality risk if not receiving an organ within 90 days. However, the donor-patient matching should also take into consideration post-transplant risk factors, such as cardiovascular disease, chronic rejection, etc., which are all common complications after transplant. Accurate prediction of these risk scores remains a significant challenge. In this study, we will use predictive models to solve the above challenge. We propose a deep learning framework model to predict multiple risk factors after a liver transplant. By formulating it as a multi-task learning problem, the proposed deep neural network was trained on this data to simultaneously predict the five post-transplant risks and achieve equally good performance by leveraging task balancing techniques. We also propose a novel fairness achieving algorithm and to ensure prediction fairness across different subpopulations. We used electronic health records of 160,360 liver transplant patients, including demographic information, clinical variables, and laboratory values, collected from the liver transplant records of the United States from 1987 to 2018. The performance of the model was evaluated using various performance metrics such as AUROC, AURPC, and accuracy. The results of our experiments demonstrate that the proposed multitask prediction model achieved high accuracy and good balance in predicting all five post-transplant risk factors, with a maximum accuracy discrepancy of only 2.7%. The fairness-achieving algorithm significantly reduced the fairness disparity compared to the baseline model.