Alle Papers – KI in der Medizin

Explainable AI (XAI) in image segmentation in medicine, industry, and beyond: A survey

Exploring the Influence of Student Interaction with ChatGPT on Critical Thinking, Problem Solving, and Creativity

ChatGPT and Generative AI Guidelines for Addressing Academic Integrity and Augmenting Pre-Existing Chatbots

Incentivizing the sharing of healthcare data in the AI Era

Economic Burden of Thalassemia Major in Iran, 2015.

Use of Large Language Models to Predict Neuroimaging

Radiology, Mobile Devices, and Internet of Things (IoT)

Establishing the Rules for Building Trustworthy AI

In Reference to “Role of Chat GPT in Public Health”, to Highlight the AI’s Incorrect Reference Generation

Voodoo Machine Learning for Clinical Predictions

Do stakeholder needs differ? - Designing stakeholder-tailored Explainable Artificial Intelligence (XAI) interfaces

Artificial Intelligence for Detecting Acute Fractures in Patients Admitted to an Emergency Department: Real-Life Performance of Three Commercial Algorithms

Advances in Artificial Intelligence

Synergistic benefits of erythropoietin and simvastatin after traumatic brain injury

Integrating artificial intelligence in healthcare: applications, challenges, and future directions

Controlling Safety of Artificial Intelligence-Based Systems in Healthcare

The future of artificial intelligence in neurosurgery: A narrative review

Advancements and Challenges in the Application of Artificial Intelligence in Surgical Arena: A Literature Review

Thyroid ultrasound image classification using a convolutional neural network

GPT-4 vs. GPT-3.5: A Concise Showdown

Artificial intelligence to identify fractures on pediatric and young adult upper extremity radiographs

A Systematic Review of Testing and Evaluation of Healthcare Applications of Large Language Models (LLMs)

Twenty-four years of empirical research on trust in AI: a bibliometric review of trends, overlooked issues, and future directions

Automated conversational agents for post-intervention follow-up: a systematic review

Feasibility of a Clinical-Radiomics Model to Predict the Outcomes of Acute Ischemic Stroke