Generating discharge summaries is a crucial yet time-consuming task in clinical practice, essential for conveying pertinent patient information and facilitating continuity of care. Recent advancements in large language models (LLMs) have significantly enhanced their capability in understanding and summarizing complex medical texts. This research aims to explore how LLMs can alleviate the burden of manual summarization, streamline workflow efficiencies, and support informed decision-making in healthcare settings. Clinical notes from a cohort of 1,099 lung cancer patients were utilized, with a subset of 50 patients for testing purposes, and 102 patients used for model fine-tuning. This study evaluates the performance of multiple LLMs, including GPT-3.5, GPT-4, GPT-4o, and LLaMA 3 8b, in generating discharge summaries. Evaluation metrics included token-level analysis (BLEU, ROUGE-1, ROUGE-2, ROUGE-L) and semantic similarity scores between model-generated summaries and physician-written gold standards. LLaMA 3 8b was further tested on clinical notes of varying lengths to examine the stability of its performance. The study found notable variations in summarization capabilities among LLMs. GPT-4o and fine-tuned LLaMA 3 demonstrated superior token-level evaluation metrics, while LLaMA 3 consistently produced concise summaries across different input lengths. Semantic similarity scores indicated GPT-4o and LLaMA 3 as leading models in capturing clinical relevance. This study contributes insights into the efficacy of LLMs for generating discharge summaries, highlighting LLaMA 3's robust performance in maintaining clarity and relevance across varying clinical contexts. These findings underscore the potential of automated summarization tools to enhance documentation precision and efficiency, ultimately improving patient care and operational capability in healthcare settings.

Lung cancer is the primary cause of cancer-related mortality, claiming approximately 1.79 million lives globally in 2020, with an estimated 2.21 million new cases diagnosed within the same period. Among these, Non-Small Cell Lung Cancer (NSCLC) is the predominant subtype, characterized by a notably bleak prognosis and low overall survival rate of approximately 25% over five years across all disease stages. However, survival outcomes vary considerably based on the stage at diagnosis and the therapeutic interventions administered. Recent advancements in artificial intelligence (AI) have revolutionized the landscape of lung cancer prognosis. AI-driven methodologies, including machine learning and deep learning algorithms, have shown promise in enhancing survival prediction accuracy by efficiently analyzing complex multi-omics data and integrating diverse clinical variables. By leveraging AI techniques, clinicians can harness comprehensive prognostic insights to tailor personalized treatment strategies, ultimately improving patient outcomes in NSCLC. Overviewing AI-driven data processing can significantly help bolster the understanding and provide better directions for using such systems.

Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective patient-centered therapies. The National Lung Screening Trial (NLST) employed computed tomography texture analysis, which provides objective measurements of texture patterns on CT scans, to quantify the mortality risks of lung cancer patients. Partially linear Cox models have gained popularity for survival analysis by dissecting the hazard function into parametric and nonparametric components, allowing for the effective incorporation of both well-established risk factors (such as age and clinical variables) and emerging risk factors (e.g., image features) within a unified framework. However, when the dimension of parametric components exceeds the sample size, the task of model fitting becomes formidable, while nonparametric modeling grapples with the curse of dimensionality. We propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the SCAD penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model. We prove the convergence and asymptotic properties of the estimator and compare it to other methods through extensive simulation studies, evaluating its performance in risk prediction and feature selection. The proposed method is applied to the NLST study dataset to uncover the effects of key clinical and imaging risk factors on patients' survival. Our findings provide valuable insights into the relationship between these factors and survival outcomes.

Ezra founder and CEO Emi Gal explains on'Fox & Friends Weekend' how artificial intelligence can'enhance' MRI scans, image quality, analysis, and comprehension. Potentially "practice-changing" results from two new cancer drug studies were introduced at the American Society of Clinical Oncology (ASCO)'s annual meeting this week in Chicago. For lung cancer patients, a drug called osimertinib -- taken by pill once daily -- was shown to reduce the risk of deaths by more than 50% in a long-running international study. For breast cancer patients, a new drug called ribociclib significantly increased survival rates and prevented recurring disease in a separate study. "Targeted therapies have been a major advance in treating deadly cancers," Dr. Marc Siegel, professor of medicine at NYU Langone Medical Center, told Fox News Digital.

A paper of Alsinglawi et al was recently accepted and published in Scientific Reports. In this paper, the authors aim to predict length of stay (LOS), discretized into either long (> 7 days) or short stays (< 7 days), of lung cancer patients in an ICU department using various machine learning techniques. The authors claim to achieve perfect results with an Area Under the Receiver Operating Characteristic curve (AUROC) of 100% with a Random Forest (RF) classifier with ADASYN class balancing over sampling technique, which if accurate could have significant implications for hospital management. However, we have identified several methodological flaws within the manuscript which cause the results to be overly optimistic and would have serious consequences if used in a clinical practice. Moreover, the reporting of the methodology is unclear and many important details are missing from the manuscript, which makes reproduction extremely difficult. We highlight the effect these oversights have had on the result and provide a more believable result of 88.91% AUROC when these oversights are corrected.

A ground breaking study using artificial intelligence is taking place at a London hospital.

Scientists have developed an artificial intelligence program that can screen people for lung cancer by analysing their blood for DNA mutations that drive the disease. The software is experimental and needs to be verified in a clinical trial, but doctors are hopeful that if it proves its worth at scale, it will boost lung cancer screening rates by making the procedure as simple as a routine blood test. The program works by examining free-floating DNA that circulates in the blood. The majority of this genetic detritus enters the bloodstream when harmless cells in the body break down and spill their molecular innards, but tumours also shed DNA as they form and grow larger. The UK has no national lung cancer screening programme, but is exploring an approach adopted in the US where people who are at high risk, such as older smokers and former smokers, can have low-dose chest X-rays to check their lungs for tumours.

Artificial intelligence and machine learning networks could help personalize radiation therapy for lung cancer, according to a new study by the Cleveland Clinic. The research, published in The Lancet Digital Health, centers around an artificial neural network built with a large dataset of patients receiving lung radiotherapy. That network, which allows each clinical center to utilize their own CT datasets to customize the framework and tailor it to their specific patient population, was built using CT scans and the electronic health records of nearly a thousand lung cancer patients treated with high-dose radiation. The company's framework uses probability estimates to select an optimized dose that prevents treatments failures to a set level, for instance a five percent probability of failure. Pre-treatment scans were input into a deep-learning model, which analyzed the scans to create an image signature that predicts treatment outcomes.

Machine learning, the most fundamental form of artificial intelligence, has started infiltrating the medical field, and it seems machines can play a crucial role in improving our health. A study of over 50 executives in the healtcare sector by TechEmergence revealed that by 2025 AI will be adopted on a broader scale. If there's one thing the healthcare industry has in abundance, it's undoubtedly data. And machine learning algorithms work better if they are exposed to more data. The savings would also be huge.

A machine that can detect cancer from a blood sample could be ready in a year. Scientists in California have developed a computer program that can detect tumour DNA as well as specify where in the body it is coming from. The program, dubbed CancerLocator, works by looking for specific molecular patterns in cancer DNA. Blood samples from 29 liver cancer patients, 12 lung cancer patients and five breast cancer patients were tested. Out of the 29 liver cancer patients, 25 had early stage cancers - which the program was able to detect in 80 per cent of cases.