AI revolution toward the cure of lung adenocarcinoma

Many advancements have been achieved by clinical researchers to progress the cure of lung cancer using artificial intelligence. More recently, Large Language Models (LLMs) have furthered these efforts with clinical decision support systems, however a more comprehensive method was required to improve the cure using multiple disciplines. In this seminal study, a Multi-LLM system was utilized to locate literature, create summaries, and provide in-depth charts. In specific, OpenAI ChatGPT 4.5 Deep research autonomously searched relevant articles from PubMed Central, MDPI, PLOS One, and other journals to provide lung adenocarcinoma summaries across 10 topics at an average of 3,200 words. Each of the summaries were visualized with charts using several Claude 3.7 Sonnet Extended code generations featuring clinical trial data. The 10 summaries were combined into a 32,000 word dataset which was inputted into 3.7 Sonnet Extended to produce 5 detailed reports, each requesting a cure to lung adenocarcinoma from different perspectives. Visualizations were obtained using Python generations of Kaplan-Meier survival curves, forest plots, and violin plots. Additionally, AI experiment runtimes were completed in 3.1 hours, accompanied by an unprecedented number of manual cross-study validations aimed towards revealing LLM transparency, reproducibility, and bias. The significance of the study is that the cure of lung adenocarcinoma was advanced with a workflow that included autonomous generations which were backed by comprehensive charts.