Results 211 to 220 of about 2,085,409 (278)

Histological Image Classification Between Follicular Lymphoma and Reactive Lymphoid Tissue Using Deep Learning and Explainable Artificial Intelligence (XAI). [PDF]

Cancers (Basel)
Carreras J, Ikoma H, Kikuti YY, Nagase S, Ito A, Orita M, Tomita S, Tanigaki Y, Nakamura N, Masugi Y.   +9 more
europepmc   +1 more source

Advancing collagen‐related pathology assessment through second‐harmonic generation imaging

Journal of Microscopy, EarlyView.
Abstract Collagen remodelling and dysregulation are the hallmarks of diverse pathological conditions. In this context, second‐harmonic generation (SHG) imaging has emerged as a powerful label‐free modality for assessing collagen. This offers submicron resolution, intrinsic optical sectioning, and deeper imaging capabilities without the need for ...
Jackson Rodrigues, Manikanth Karnati, Gagan Raju, Edwin Pious, Ankur Gogoi, Guan‐Yu Zhuo, Nirmal Mazumder, Fu‐Jen Kao   +7 more
wiley   +1 more source

AI Presents Both Problems and Opportunities for Minorities

Journal of Management Studies, EarlyView.
Abstract The recent Point article by Wu (2026) calls for a better understanding of factors that could lead to the development of AI systems that are likely to perpetuate social inequality. The Point article introduces the idea that computational beliefs interact with computational inequalities in a more systematic manner that develops AI systems which ...
María del Carmen Triana, Arun Upadhyay
wiley   +1 more source

AI foundation models in plant biology

New Phytologist, EarlyView.
Foundation models decode genomes, engineer proteins, phenotype crops, and drive AI agents across plant biology. This figure was created in BioRender (BioRender.com/xcmmkel). Summary Rapid technological progress has enabled plant biologists to accumulate unprecedented volumes of multi‐scale, multi‐modal data, yet this abundance of data has intensified ...
Haopeng Yu
wiley   +1 more source

Artificial Intelligence in Orthodontics: A Scoping Review of Methods and Applications

Orthodontics &Craniofacial Research, EarlyView.
ABSTRACT The rapid integration of artificial intelligence (AI) into orthodontics raises important questions about which methods are being applied, how they are being validated, and what barriers remain to clinical implementation. This study was conducted to inform orthodontic practitioners, researchers, and educators about the possibilities that AI ...
Yodhathai Satravaha, Chaiyapol Chaweewannakorn, Kawin Sipiyaruk, Supatchai Boonpratham, Ard Likitkulthanaporn, Supakit Peanchitlertkajorn, Zaw Htet Aung   +6 more
wiley   +1 more source

eXplainable Artificial Intelligence (XAI) for improving organisational regility. [PDF]

PLoS One
Shafiabady N, Hadjinicolaou N, Hettikankanamage N, MohammadiSavadkoohi E, Wu RMX, Vakilian J.   +5 more
europepmc   +1 more source

Artificial Intelligence in Orthodontics: Part 3—Cautions, Limitations and Future Prospects

Orthodontics &Craniofacial Research, EarlyView.
ABSTRACT Artificial intelligence (AI) is increasingly affecting orthodontic practice and research, yet its adoption requires a careful understanding of both its capabilities and its limitations. As the final part of a three‐section review, this paper focuses on the practical cautions and constraints that accompany current AI developments in ...
Fatemeh Sohrabniya, Julian Woolley, Khuram Naveed, Ruben Pauwels, Peter Bangsgaard Stoustrup, Hossein Mohammad‐Rahimi   +5 more
wiley   +1 more source

Artificial Intelligence‐Driven Precision Bisphosphonate Therapy in Osteoporosis: Mechanisms, Clinical Applications, Risks, and Future Directions

Orthopaedic Surgery, EarlyView.
This comprehensive review integrates 17 benchmark studies to illustrate the transition of osteoporosis management toward precision medicine. The schematic highlights three key clinical applications: AI‐driven opportunistic screening from routine CTs, precision pharmacotherapy selection, and the predictive prevention of adverse events like MRONJ ...
Xiang Yao, Yicong Chao, Xiao Gu, Jingtao Peng, Jishan Yuan, Wei Shi, Fuguo Zhang, Qiwen Bao   +7 more
wiley   +1 more source

Explainable artificial intelligence (XAI) to find optimal in-silico biomarkers for cardiac drug toxicity evaluation. [PDF]

Sci Rep
Pramudito MA, Fuadah YN, Qauli AI, Marcellinus A, Lim KM.   +4 more
europepmc   +1 more source

From Arabidopsis to Crop Prediction: Enrichment and Limitations of Tree‐Based SHAP for Marker Prioritization in Plant Breeding

Plant Breeding, EarlyView.
ABSTRACT Genomic prediction is widely used in plant breeding, but feature‐attribution scores from predictive models are sometimes interpreted as evidence for trait‐associated or causal loci. We evaluated whether global SHapley Additive exPlanations (SHAP) from XGBoost and LightGBM prioritize markers or genomic windows enriched for signals from mixed ...
Francisco Orts
wiley   +1 more source