AIMC Topic: Humans

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AI: Promise or Peril for Patient Safety.

Journal of patient safety
Patient safety advocates identify concerns for the impact of AI on patient safety. Patients identified the following 4 main areas that AI developers, regulatory bodies, and clinical users of AI are asked to consider: data integrity and bias, efficacy...

Comprehensive Management of Intracranial Aneurysms Using Artificial Intelligence: An Overview.

World neurosurgery
Intracranial aneurysms (IAs), an asymptomatic vascular lesion, are becoming increasingly common as imaging technology progresses. Subarachnoid hemorrhage from IAs rupture entails a substantial risk of mortality or severe disability. The early detecti...

PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasets.

IEEE transactions on visualization and computer graphics
Acute stroke demands prompt diagnosis and treatment to achieve optimal patient outcomes. However, the intricate and irregular nature of clinical data associated with acute stroke, particularly blood pressure (BP) measurements, presents substantial ob...

SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for GNN-Based Synthetic Lethal Prediction.

IEEE transactions on visualization and computer graphics
Synthetic Lethal (SL) relationships, though rare among the vast array of gene combinations, hold substantial promise for targeted cancer therapy. Despite advancements in AI model accuracy, there is still a significant need among domain experts for in...

How Aligned are Human Chart Takeaways and LLM Predictions? A Case Study on Bar Charts with Varying Layouts.

IEEE transactions on visualization and computer graphics
Large Language Models (LLMs) have been adopted for a variety of visualizations tasks, but how far are we from perceptually aware LLMs that can predict human takeaways? Graphical perception literature has shown that human chart takeaways are sensitive...

AdversaFlow: Visual Red Teaming for Large Language Models with Multi-Level Adversarial Flow.

IEEE transactions on visualization and computer graphics
Large Language Models (LLMs) are powerful but also raise significant security concerns, particularly regarding the harm they can cause, such as generating fake news that manipulates public opinion on social media and providing responses to unethical ...

How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?

IEEE transactions on visualization and computer graphics
In this study, we address the growing issue of misleading charts, a prevalent problem that undermines the integrity of information dissemination. Misleading charts can distort the viewer's perception of data, leading to misinterpretations and decisio...

HuBar: A Visual Analytics Tool to Explore Human Behavior Based on fNIRS in AR Guidance Systems.

IEEE transactions on visualization and computer graphics
The concept of an intelligent augmented reality (AR) assistant has significant, wide-ranging applications, with potential uses in medicine, military, and mechanics domains. Such an assistant must be able to perceive the environment and actions, reaso...

Towards Dataset-Scale and Feature-Oriented Evaluation of Text Summarization in Large Language Model Prompts.

IEEE transactions on visualization and computer graphics
Recent advancements in Large Language Models (LLMs) and Prompt Engineering have made chatbot customization more accessible, significantly reducing barriers to tasks that previously required programming skills. However, prompt evaluation, especially a...

A machine learning-based analysis of nationwide cancer comprehensive genomic profiling data across cancer types to identify features associated with recommendation of genome-matched therapy.

ESMO open
BACKGROUND: The low probability of identifying druggable mutations through comprehensive genomic profiling (CGP) and its financial and time costs hinder its widespread adoption. To enhance the effectiveness and efficiency of cancer precision medicine...