Gpt (generative pre-trained transformer)–a comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions

G Yenduri, M Ramalingam, GC Selvi, Y Supriya… - IEEE …, 2024 - ieeexplore.ieee.org
The Generative Pre-trained Transformer (GPT) represents a notable breakthrough in the
domain of natural language processing, which is propelling us toward the development of …

Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges

DH Lee, SN Yoon - International journal of environmental research and …, 2021 - mdpi.com
This study examines the current state of artificial intelligence (AI)-based technology
applications and their impact on the healthcare industry. In addition to a thorough review of …

AI applications to medical images: From machine learning to deep learning

I Castiglioni, L Rundo, M Codari, G Di Leo, C Salvatore… - Physica medica, 2021 - Elsevier
Purpose Artificial intelligence (AI) models are playing an increasing role in biomedical
research and healthcare services. This review focuses on challenges points to be clarified …

Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

MJ Iqbal, Z Javed, H Sadia, IA Qureshi, A Irshad… - Cancer cell …, 2021 - Springer
Artificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive
abilities and to address difficult healthcare challenges including complex biological …

Artificial convolutional neural network in object detection and semantic segmentation for medical imaging analysis

R Yang, Y Yu - Frontiers in oncology, 2021 - frontiersin.org
In the era of digital medicine, a vast number of medical images are produced every day.
There is a great demand for intelligent equipment for adjuvant diagnosis to assist medical …

Statistical significance: p value, 0.05 threshold, and applications to radiomics—reasons for a conservative approach

G Di Leo, F Sardanelli - European radiology experimental, 2020 - Springer
Here, we summarise the unresolved debate about p value and its dichotomisation. We
present the statement of the American Statistical Association against the misuse of statistical …

Federated learning for predicting clinical outcomes in patients with COVID-19

I Dayan, HR Roth, A Zhong, A Harouni, A Gentili… - Nature medicine, 2021 - nature.com
Federated learning (FL) is a method used for training artificial intelligence models with data
from multiple sources while maintaining data anonymity, thus removing many barriers to …

Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints

O Oren, BJ Gersh, DL Bhatt - The Lancet Digital Health, 2020 - thelancet.com
Artificial intelligence (AI) is a disruptive technology that involves the use of computerised
algorithms to dissect complicated data. Among the most promising clinical applications of AI …

The application of Industry 4.0 technologies in sustainable logistics: a systematic literature review (2012–2020) to explore future research opportunities

X Sun, H Yu, WD Solvang, Y Wang, K Wang - Environmental Science and …, 2022 - Springer
Nowadays, the market competition becomes increasingly fierce due to diversified customer
needs, stringent environmental requirements, and global competitors. One of the most …

Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States

F Pesapane, C Volonté, M Codari, F Sardanelli - Insights into imaging, 2018 - Springer
Worldwide interest in artificial intelligence (AI) applications is growing rapidly. In medicine,
devices based on machine/deep learning have proliferated, especially for image analysis …