Medical decision-making often involves integrating knowledge from multiple clinical specialties, typically achieved through multidisciplinary teams. Inspired by this collaborative process, recent work has leveraged large language models (LLMs) in multi-agent collaboration frameworks to emulate expert teamwork. While these approaches improve reasoning through agent interaction, they are limited by static, pre-assigned roles, which hinder adaptability and dynamic knowledge integration. To address these limitations, we propose KAMAC, a Knowledge-driven Adaptive Multi-Agent Collaboration framework that enables LLM agents to dynamically form and expand expert teams based on the evolving diagnostic context. KAMAC begins with one or more expert agents and then conducts a knowledge-driven discussion to identify and fill knowledge gaps by recruiting additional specialists as needed. This supports flexible, scalable collaboration in complex clinical scenarios, with decisions finalized through reviewing updated agent comments. Experiments on two real-world medical benchmarks demonstrate that KAMAC significantly outperforms both single-agent and advanced multi-agent methods, particularly in complex clinical scenarios (i.e., cancer prognosis) requiring dynamic, cross-specialty expertise. Our code is publicly available at: https://github.com/XiaoXiao-Woo/KAMAC.

Large language models (LLMs) have demonstrated potential in the innovation of many disciplines. However, how they can best be developed for oncology remains underdeveloped. State-of-the-art OpenAI models were fine-tuned on a clinical dataset and clinical guidelines text corpus for two important cancer treatment factors, adjuvant radiation therapy and chemotherapy, using a novel Langchain prompt engineering pipeline. A high accuracy (0.85+) was achieved in the classification of adjuvant radiation therapy and chemotherapy for breast cancer patients. Furthermore, a confidence interval was formed from observational data on the quality of treatment from human oncologists to estimate the proportion of scenarios in which the model must outperform the original oncologist in its treatment prediction to be a better solution overall as 8.2% to 13.3%. Due to indeterminacy in the outcomes of cancer treatment decisions, future investigation, potentially a clinical trial, would be required to determine if this threshold was met by the models. Nevertheless, with 85% of U.S. cancer patients receiving treatment at local community facilities, these kinds of models could play an important part in expanding access to quality care with outcomes that lie, at minimum, close to a human oncologist.

Documentation burden is a major contributor to clinician burnout, which is rising nationally and is an urgent threat to our ability to care for patients. Artificial intelligence (AI) chatbots, such as ChatGPT, could reduce clinician burden by assisting with documentation. Although many hospitals are actively integrating such systems into electronic medical record systems, AI chatbots utility and impact on clinical decision-making have not been studied for this intended use. We are the first to examine the utility of large language models in assisting clinicians draft responses to patient questions. In our two-stage cross-sectional study, 6 oncologists responded to 100 realistic synthetic cancer patient scenarios and portal messages developed to reflect common medical situations, first manually, then with AI assistance. We find AI-assisted responses were longer, less readable, but provided acceptable drafts without edits 58% of time. AI assistance improved efficiency 77% of time, with low harm risk (82% safe). However, 7.7% unedited AI responses could severely harm. In 31% cases, physicians thought AI drafts were human-written. AI assistance led to more patient education recommendations, fewer clinical actions than manual responses. Results show promise for AI to improve clinician efficiency and patient care through assisting documentation, if used judiciously. Monitoring model outputs and human-AI interaction remains crucial for safe implementation.

AI chatbots that answer medical queries could save doctors' time, but also run the risk of making recommendations that harm the people seeking advice. Shan Chen at Harvard University and his colleagues conducted an experiment in which six oncologists addressed a variety of questions from 100 fictional people with cancer, who the doctors knew were not real. The questions were presented via a hospital electronic messaging system.

Purely medical cancer screening methods are often costly, time-consuming, and weakly applicable on a large scale. Advanced Artificial Intelligence (AI) methods greatly help cancer detection but require specific or deep medical data. These aspects affect the mass implementation of cancer screening methods. For these reasons, it is a disruptive change for healthcare to apply AI methods for mass personalized assessment of the cancer risk among patients based on the existing Electronic Health Records (EHR) volume. This paper presents a novel method for mass cancer risk prediction using EHR data. Among other methods, our one stands out by the minimum data greedy policy, requiring only a history of medical service codes and diagnoses from EHR. We formulate the problem as a binary classification. This dataset contains 175 441 de-identified patients (2 861 diagnosed with cancer). As a baseline, we implement a solution based on a recurrent neural network (RNN). We propose a method that combines machine learning and survival analysis since these approaches are less computationally heavy, can be combined into an ensemble (the Survival Ensemble), and can be reproduced in most medical institutions. We test the Survival Ensemble in some studies. Firstly, we obtain a significant difference between values of the primary metric (Average Precision) with 22.8% (ROC AUC 83.7%, F1 17.8%) for the Survival Ensemble versus 15.1% (ROC AUC 84.9%, F1 21.4%) for the Baseline. Secondly, the performance of the Survival Ensemble is also confirmed during the ablation study. Thirdly, our method exceeds age baselines by a significant margin. Fourthly, in the blind retrospective out-of-time experiment, the proposed method is reliable in cancer patient detection (9 out of 100 selected). Such results exceed the estimates of medical screenings, e.g., the best Number Needed to Screen (9 out of 1000 screenings).

Predicting cancer patient survival rates is a crucial aspect of cancer treatment and management. Accurately forecasting a patient's prognosis helps medical professionals make informed decisions about the most appropriate course of action and can also aid in the development of personalized treatment plans. Researchers from the University of British Columbia and BC Cancer have created an AI model that predicts cancer patient survival with greater accuracy and using more readily accessible data compared to previous methods. The AI model utilizes natural language processing (NLP), a field of AI that comprehends human language, to examine oncologists' notes taken following a patient's initial consultation. This is the first step in a cancer patient's journey after diagnosis.

A team of researchers from the University of British Columbia and BC Cancer have developed an artificial intelligence (AI) model that predicts cancer patient survival more accurately and with more readily available data than previous tools. The model uses natural language processing (NLP)--a branch of AI that understands complex human language--to analyze oncologist notes following a patient's initial consultation visit--the first step in the cancer journey after diagnosis. By identifying characteristics unique to each patient, the model was shown to predict six-month, 36-month and 60-month survival with greater than 80 percent accuracy. The findings were published today in JAMA Network Open. "Predicting cancer survival is an important factor that can be used to improve cancer care," said lead author Dr. John-Jose Nunez, a psychiatrist and clinical research fellow with the UBC Mood Disorders Centre and BC Cancer.

In this article, we will explain our approach to tracking healthy organs in Gastrointestinal Tract MRI scans with the aim of improving gastrointestinal tract cancer treatment. Approximately 5 million new cases of gastrointestinal cancer are reported every year around the world, and 3.4 million result in deaths. Of these patients, only about half are eligible for radiation therapy. Radiation therapy necessitates the delivery of high doses of X-ray beam radiation pointed at tumors. Radiation oncologists must try to avoid the stomach and intestines while administering the treatment.

Researchers at Duke University Medical Center have developed a deep-learning-based computer-aided detection (CAD) system to identify difficult-to-detect brain metastases on MR images. The algorithm exhibited excellent sensitivity and specificity, outperforming other CAD systems in development. The tool shows potential to enable earlier identification of emerging brain metastases, allowing them to be targeted with stereotactic radiosurgery (SRS) when they first appear and, for some patients, reducing the number of required treatments. SRS, which uses precisely focused photon beams to deliver a high dose of radiation to targets in the brain in a single radiotherapy session, is evolving into the standard-of-care treatment for patients with a limited number of brain metastases. To target a metastasis, however, it must first be identified on an MR image.

MANILA, Philippines – Filipino-founded biotech firm InterVenn Biosciences on Friday, July 22, launched its Philippine office. Though the company is headquartered in San Francisco, California, the team that developed and maintains the proprietary AI technology that speeds up certain research processes for the company is made up mostly of Filipinos residing in the Philippines, including one of its founders, AI and blockchain expert Aldo Carrascoso. The company has about 150 Filipinos working in the Philippines, majority of whom are software developers, and who make up half of the company. Through the help of the company's advanced AI platform, the company is able to help its global groups of researchers and scientists significantly reduce the time it takes for some processes such as analysis of samples from months to seconds. It's through this meaningful application of AI that the firm has been able to make strides in the field since its founding in 2017, as well as strides in funding.