Results 101 to 110 of about 24,617 (284)

Shaping the Future of Radiography Education: Lessons From ChatGPT and Generative AI

Journal of Medical Radiation Sciences, EarlyView.
ChatGPT can provide structured guidance, support self‐assessment and scaffold learning processes that bridge classroom knowledge and clinical expectations. However, AI must be embedded in ways that uphold the core principles of radiographic practice: accuracy, reflective judgment, ethical reasoning, empathy and patient‐centred care.
Minh T. Chau, Haydn Kerr, Clare L. Singh, Bismark Ofori‐Manteaw, Elio Arruzza, Kelly Bentley‐Spuur   +5 more
wiley   +1 more source

Explainable Artificial Intelligence (XAI) for 6G: Improving Trust between Human and Machine [PDF]

, 2019
Weisi Guo
openalex   +1 more source

Explainable Artificial Intelligence (XAI): Classification of Medical Thermal Images of Neonates Using Class Activation Maps

, 2021
Ahmet Haydar Örnek, Murat Ceylan
openalex   +2 more sources

Should Dermatologists Recommend Direct‐to‐Consumer App‐Based Remote Diagnostics? An Ethical Analysis

JEADV Clinical Practice, EarlyView.
ABSTRACT Background Dermatology patients still face barriers in accessing timely specialist care. As direct‐to‐consumer (DTC) apps for remote dermatological diagnostics proliferate, guidance is lacking. While promising efficiency and efficacy, their clinical—and ethical—legitimacy is not yet well established.
Sonja Mathes, Sebastian Seurig, Theresa Willem, Marta Dechant, Stephan Traidl, Mina Kang, Pedro Marques Pinto, Annette Binder, Tilo Biedermann, Ulf Darsow   +9 more
wiley   +1 more source

Identification of kidney stones in KUB X-ray images using VGG16 empowered with explainable artificial intelligence

Scientific Reports
A kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for
Fahad Ahmed, Sagheer Abbas, Atifa Athar, Tariq Shahzad, Wasim Ahmad Khan, Meshal Alharbi, Muhammad Adnan Khan, Arfan Ahmed   +7 more
doaj   +1 more source

Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation

Frontiers in Neuroscience, 2019
The use of Artificial Intelligence and machine learning in basic research and clinical neuroscience is increasing. AI methods enable the interpretation of large multimodal datasets that can provide unbiased insights into the fundamental principles of ...
Jean-Marc Fellous, Jean-Marc Fellous, Guillermo Sapiro, Andrew Rossi, Helen Mayberg, Michele Ferrante, Michele Ferrante   +6 more
doaj   +1 more source

Interpretation of cluster structures in pain‐related phenotype data using explainable artificial intelligence (XAI) [PDF]

, 2020
Jörn Lötsch, Sebastian Malkusch
openalex   +1 more source

Machine learning‐based predictive models versus traditional risk scores in hemodialysis patients with comorbid urolithiasis

Precision Medical Sciences, EarlyView.
Machine learning‐based predictive models outperform traditional risk scores in hemodialysis patients with comorbid urolithiasis by capturing nonlinear, dialysis‐specific interactions. These approaches enable more accurate prediction of stone recurrence, sepsis, hospitalization, and mortality, supporting personalized risk stratification and precision ...
Dipal Chaulagain, Muhammad Riaz, Mirza Muhammad Hadeed Khawar, Jawad Hameed, Malik Sohail Riaz   +4 more
wiley   +1 more source

AI Competency and Perception of XAI Importance Versus Attitude Toward AI: Mediating Effects of Belief in XAI Availability

Advances in Human-Computer Interaction
In this research, we hypothesize that attitudes toward artificial intelligence (AI) are shaped by individuals’ perceived competence in using and managing it, as well as their assessment of the importance of AI’s understandability and transparency, often ...
M. Liebherr, A. Babiker, S. AlShakhsi, D. Al-Thani, C. Montag, R. Ali   +5 more
doaj   +1 more source

How AI and Digital Technologies Can Enhance Sustainable Livestock Manure Management: An Overview From Treatment to Distribution

Sustainable Development, EarlyView.
ABSTRACT Sustainable livestock manure management sits at the nexus of climate, nutrient circularity and water quality. This review explores how artificial intelligence (AI) and digital platforms are used across four management stages, that is, treatment, storage, valorisation and distribution, and figures out where integration fails to deliver ...
Zhan Shi, Feihong Liang, Francesco Marinello, Andrea Pezzuolo   +3 more
wiley   +1 more source