Results 221 to 230 of about 20,583 (298)

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

Advanced Science, Volume 13, Issue 35, 24 June 2026.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu, Mengtao Sun, Lusheng Li, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source

The Emerging Role of Explainable Artificial Intelligence in EEG-Based Autism Research: A Systematic Review. [PDF]

NeuroSci
Martelli ME, Colella S, Meloni R, Gigliotti F, Rosato A, Panella M, Sogos C.   +6 more
europepmc   +1 more source

On Using the Shapley Value for Anomaly Localization: A Statistical Investigation

Applied AI Letters, Volume 7, Issue 2, June 2026.
ABSTRACT Recent publications have suggested using the Shapley value for anomaly localization for sensor data systems. We use a reasonable statistical model for the classifiers required to compute the Shapley value to provide repeatable and rigorous analysis in the anomaly localization application.
Rick S. Blum, Xubin Fang, Franziska Freytag   +2 more
wiley   +1 more source

Explainable AI in healthcare: a systematic review of XAI use cases in imaging, diagnostics, and rehabilitation. [PDF]

Front Artif Intell
Aravindkumar A, Ramadoss M, Fakhruddin Ahmed SA, Sampath V, Lakshminarayanan K.   +4 more
europepmc   +1 more source

User‐Based Evaluation of Explainability Techniques for Misogyny Detection in Code‐Mixed Hindi–English

Applied AI Letters, Volume 7, Issue 2, June 2026.
In this work, we have performed human‐based evaluation of three post hoc explainability techniques, Local Interpretable Model Agnostic Explanations (LIME), Shapely Additive Explanations (SHAP), and integrated Gradients (IG) for a multilingual Bidirectional Encoder Representations from Transformers (mBERT) based binary and multi‐label misogyny ...
Sargam Yadav, Abhishek Kaushik, Kevin McDaid   +2 more
wiley   +1 more source

Cross‐Method Explanation Stability Under Prediction‐Preserving Perturbations in Explainable AI

Applied AI Letters, Volume 7, Issue 2, June 2026.
The cross‐method analysis showed common vulnerability patterns across gradient‐based and perturbation‐based explainers, whereas Grad‐CAM demonstrated a specific ability to be resilient. Further discussion revealed that, before prediction changes with increasing ε, explanation divergence could already have commenced, indicating that further explanation ...
Muhammad Hasnain, Tooba Gul, Muhammad Fawad Ahmed, Israr Ghani, Imran Ghani   +4 more
wiley   +1 more source

Symmetry-guided explainable deep learning for colon cancer diagnosis: model benchmarking, cross-validation, statistical analysis, and explainability via ablation studies. [PDF]

Front Artif Intell
Solanki A, Gopani D, Mangla M, Sharma N, Musa MJ, Patibandla RSML.   +5 more
europepmc   +1 more source

Stacked Ensemble Model With Explainable AI for Early Detection of Heart Disease

Analytical Science Advances, Volume 7, Issue 1, June 2026.
ABSTRACT Heart disease (HD) is still one of the most common causes of death around the world. Early detection is very important, but it is often hard to do because the symptoms are not specific and the models are not very clear. We propose a two‐layer stacked ensemble that combines four base learners—Support Vector Machine, K‐Nearest Neighbors, Naïve ...
Nazmun Nahar, Sanjatul Hasan Siam, Joy Bhowmik, Ayesha Nasrin Ripa, Md. Hasan Imam, Haw Jiunn Woo, Hamid Osman, Mayeen Uddin Khandaker, Shams Forruque Ahmed   +8 more
wiley   +1 more source

Emotion-Adaptive Large Language Model-Driven Clinical Decision Support: User Evaluation of the Empathic Clinical Decision Support System Framework for Trust and Explainability. [PDF]

JMIR Hum Factors
Zhang T, Bae SW, Chung T, Dey AK.
europepmc   +1 more source

Addressing Small Data Challenges in Biopharmaceutical Development and Manufacturing: A Mini Review of Multi‐Fidelity Techniques

Biotechnology and Bioengineering, Volume 123, Issue 6, Page 1465-1480, June 2026.
ABSTRACT The growing demand for biopharmaceutical products reflects their effectiveness in medical treatments. However, developing new biopharmaceuticals remains a major bottleneck, often taking up to a decade before market approval. Machine learning (ML) models have the potential to accelerate this process, but their success depends on access to large
Mohammad Golzarijalal, Uwe Aickelin, Ellen Otte   +2 more
wiley   +1 more source