Clinical calculators play a vital role in healthcare by offering accurate evidence-based predictions for various purposes such as prognosis. Nevertheless, their widespread utilization is frequently hindered by usability challenges, poor dissemination, and restricted functionality. Augmenting large language models with extensive collections of clinical calculators presents an opportunity to overcome these obstacles and improve workflow efficiency, but the scalability of the manual curation process poses a significant challenge. In response, we introduce AgentMD, a novel language agent capable of curating and applying clinical calculators across various clinical contexts. Using the published literature, AgentMD has automatically curated a collection of 2,164 diverse clinical calculators with executable functions and structured documentation, collectively named RiskCalcs. Manual evaluations show that RiskCalcs tools achieve an accuracy of over 80% on three quality metrics. At inference time, AgentMD can automatically select and apply the relevant RiskCalcs tools given any patient description. On the newly established RiskQA benchmark, AgentMD significantly outperforms chain-of-thought prompting with GPT-4 (87.7% vs. 40.9% in accuracy). Additionally, we also applied AgentMD to real-world clinical notes for analyzing both population-level and risk-level patient characteristics. In summary, our study illustrates the utility of language agents augmented with clinical calculators for healthcare analytics and patient care.

Researchers developed a deep learning model using artificial intelligence(AI) for the current study. The team used a CXR-CVD system that was "trained" to search more than 147,000 chest X-ray images from almost 41,000 participants in a cancer screening trial and spot patterns associated with cardiovascular disease. Once developed, the system could predict a person's 10-year risk of having a stroke or heart attack from a single chest X-ray. Lead study author Jakob Weiss, Ph.D., a radiologist with the Cardiovascular Imaging Research Center at Massachusetts General Hospital and the AI in Medicine program at the Brigham and Women's Hospital in Boston, explained to MNT: "Current guidelines of the American College of Cardiology and American Heart Association on the primary prevention of cardiovascular disease recommend the use of a risk calculator to estimate the risk of future CVD. This risk calculator is based on the ASCVD risk score, a multivariable regression model requiring nine variables as input, such as age, sex, smoking, lipids, blood pressure, and diabetes. However, these variables are often not available, which makes novel and more practical screening approaches desirable."

Patients could receive a greater benefit from open heart surgery thanks to a new computer model aimed at helping surgeons to better calculate risk and decide whether it's safe to operate. This project, jointly funded by the British Heart Foundation (BHF) and The Alan Turing Institute, will develop a new platform using machine learning to identify patients who are most likely to have a successful operation. Over 30,000 adult patients are considered for heart surgery every year in the UK, and risk prediction plays a major role in the decision-making process made by doctors and patients. Assessing risk before open heart surgery is crucial due to the potential complications that can arise during and after the operation. To calculate a patient's risk before surgery, heart surgeons currently use models such as the EuroSCORE – but this may overestimate the actual risk, partly due to improvements in patient management since the model was developed.

Heart failure (HF) is a leading cause of death. Early intervention is the key to reduce HF-related morbidity and mortality. Data from the baseline visits (1987–89) of the Atherosclerosis Risk in Communities (ARIC) study was used. Incident hospitalized HF events were ascertained by ICD codes. Participants with good quality baseline ECGs were included. Participants with prevalent HF were excluded. ECG-artificial intelligence (AI) model to predict HF was created as a deep residual convolutional neural network (CNN) utilizing standard 12-lead ECG. The area under the receiver operating characteristic curve (AUC) was used to evaluate prediction models including (CNN), light gradient boosting machines (LGBM), and Cox proportional hazards regression. A total of 14 613 (45% male, 73% of white, mean age standard deviation of 54 5) participants were eligible.

Our nation has been embroiled in social unrest for several years, reaching a boiling point in May 2020 with the death of George Floyd. Health care is not immune to such inequalities. The United States is facing a crisis in health disparity -- the unequal burden of illness, injury or mortality experienced among population groups. Research by the W.K. Kellogg Foundation and Altarum, a nonprofit organization dedicated to advancing health among at-risk and disenfranchised populations, estimates that disparities lead to approximately $93 billion in excess medical care costs and $42 billion in lost productivity per year. Close to home, Cleveland rightfully boasts its role as a leading medical destination.

The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at a steep economic price. We design analytical tools to support these decisions and combat the pandemic. Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact. By leveraging cohort-level clinical data, patient-level hospital data, and census-level epidemiological data, we develop an integrated four-step approach, combining descriptive, predictive and prescriptive analytics. First, we aggregate hundreds of clinical studies into the most comprehensive database on COVID-19 to paint a new macroscopic picture of the disease. Second, we build personalized calculators to predict the risk of infection and mortality as a function of demographics, symptoms, comorbidities, and lab values. Third, we develop a novel epidemiological model to project the pandemic's spread and inform social distancing policies. Fourth, we propose an optimization model to re-allocate ventilators and alleviate shortages. Our results have been used at the clinical level by several hospitals to triage patients, guide care management, plan ICU capacity, and re-distribute ventilators. At the policy level, they are currently supporting safe back-to-work policies at a major institution and equitable vaccine distribution planning at a major pharmaceutical company, and have been integrated into the US Center for Disease Control's pandemic forecast.

A retinal fundus image is a photograph of the back of the eye taken through the pupil. For more than 100 years these images have been used for detecting eye disease. Now Google has introduced a surprising new use for retinal images: combined with artificial intelligence, they can also predict a patient's risk of heart attack or stoke. Research arm Google Brain today published a paper in the journal Nature Biomedical Engineering which demonstrates how deep learning models can use retinal images to detect a patient's age, gender, smoking status and systolic blood pressure; calculate cardiovascular risk factors; and predict the risk of major adverse cardiac events occurring over the next five years. A problem with today's mainstream cardiovascular risk calculators such as the Pooled Cohort Equations, Framingham, and Systematic Coronary Risk Evaluation is that they require the input of multiple features such as blood pressure, body mass index, glucose and cholesterol levels, etc. to generate a disease risk result. A study by the American College of Cardiology's Practice Innovation And Clinical Excellence Program concluded that the data required to calculate 10-year risk was available for less than 30% of patients.

In the future, doctors will be able to diagnose your illness before even meeting you. Recent applications of machine learning with big data are able to predict diseases--such as Alzheimer's and diabetes--with incredible accuracy, years before the onset of symptoms. To assess the likelihood of a patient developing a certain condition, physicians have traditionally relied on risk calculators such as this one. These use basic patient info such as age, weight and blood pressure to quantify the probability of occurrence of the disease in the future. A risk calculator is a simple tool that works using an equation based on the data of one or a few studies; in fact, doctors used these mathematical tools on paper back in the day, simply solving for X after filling the blanks with a patient's data.

Verily and its sister company Google Research are developing an AI-powered test for heart disease that analyses retinal imagery. Verily is Google's standalone healthcare division, and has embarked on many collaborations since being spun out in 2015, and AI is emerging as one of the most exciting areas in digital health. The paper, which is yet to be peer reviewed, describes a technology that analyses imagery of the inner surface of the back of the eye to identify signs of heart disease. The system was originally trained using images from 284,335 heart disease patients, during which time it identified features specific to the disease through machine learning. The test was then validated using two independent datasets from 12,026 and 999 patients respectively. According to the authors of the paper, the system could accurately identify a number of heart disease-related risk factors, such as smoking status, HbA1c levels, blood pressure, and past major cardiac events as well as age and gender.