Alan B. McMillan

Tyler J. Bradshaw, Alan B. McMillan

The influence of artificial intelligence (AI) within the field of nuclear medicine has been rapidly growing. Many researchers and clinicians are seeking to apply AI within PET, and clinicians will soon find themselves engaging with AI-based applications all along the chain of molecular imaging, from image reconstruction to enhanced reporting. This expanding presence of AI in PET imaging will result in greater demand for educational resources for those unfamiliar with AI. The objective of this article to is provide an illustrated guide to the core principles of modern AI, with specific focus on aspects that are most likely to be encountered in PET imaging. We describe convolutional neural networks, algorithm training, and explain the components of the commonly used U-Net for segmentation and image synthesis.

Zhijin He, Alan B. McMillan

The application of artificial intelligence (AI) in medical imaging has revolutionized diagnostic practices, enabling advanced analysis and interpretation of radiological data. This study presents a comprehensive evaluation of radiomics-based and deep learning-based approaches for disease detection in chest radiography, focusing on COVID-19, lung opacity, and viral pneumonia. While deep learning models, particularly convolutional neural networks and vision transformers, learn directly from image data, radiomics-based models extract handcrafted features, offering potential advantages in data-limited scenarios. We systematically compared the diagnostic performance of various AI models, including Decision Trees, Gradient Boosting, Random Forests, Support Vector Machines, and Multi-Layer Perceptrons for radiomics, against state-of-the-art deep learning models such as InceptionV3, EfficientNetL, and ConvNeXtXLarge. Performance was evaluated across multiple sample sizes. At 24 samples, EfficientNetL achieved an AUC of 0.839, outperforming SVM (AUC = 0.762). At 4000 samples, InceptionV3 achieved the highest AUC of 0.996, compared to 0.885 for Random Forest. A Scheirer-Ray-Hare test confirmed significant main and interaction effects of model type and sample size on all metrics. Post hoc Mann-Whitney U tests with Bonferroni correction further revealed consistent performance advantages for deep learning models across most conditions. These findings provide statistically validated, data-driven recommendations for model selection in diagnostic AI. Deep learning models demonstrated higher performance and better scalability with increasing data availability, while radiomics-based models may remain useful in low-data contexts. This study addresses a critical gap in AI-based diagnostic research by offering practical guidance for deploying AI models across diverse clinical environments.

Raj Hansini Khoiwal, Alan B. McMillan

Developing artificial intelligence (AI) and machine learning (ML) models for medical imaging typically involves extensive training and testing on large datasets, consuming significant computational time, energy, and resources. There is a need for more efficient methods that can achieve comparable or superior diagnostic performance without the associated resource burden. We investigated the feasibility of replacing conventional training procedures with an embedding-based approach that leverages concise and semantically meaningful representations of medical images. Using pre-trained foundational models-specifically, convolutional neural networks (CNN) like ResNet and multimodal models like Contrastive Language-Image Pre-training (CLIP)-we generated image embeddings for multi-class classification tasks. Simple linear classifiers were then applied to these embeddings. The approach was evaluated across diverse medical imaging modalities, including retinal images, mammography, dermatoscopic images, and chest radiographs. Performance was compared to benchmark models trained and tested using traditional methods. The embedding-based models surpassed the benchmark area under the receiver operating characteristic curve (AUC-ROC) scores by up to 87 percentage in multi-class classification tasks across the various medical imaging modalities. Notably, CLIP embedding models achieved the highest AUC-ROC scores, demonstrating superior classification performance while significantly reducing computational demands. Our study indicates that leveraging embeddings from pre-trained foundational models can effectively replace conventional, resource-intensive training and testing procedures in medical image analysis. This embedding-based approach offers a more efficient alternative for image segmentation, classification, and prediction, potentially accelerating AI technology integration into clinical practice.