From:  How algorithms are transforming the diagnosis of ischemic heart disease—state of the art

 Emerging AI technologies in cardiac imaging.

TechnologyTechnical descriptionPotential benefitsDevelopment stageReferences
Transformer networksAdvanced deep learning architectures using self-attention mechanisms for sequence modeling and temporal pattern recognitionSequential data analysis; modeling temporal dependencies in cardiac cycles; multi-scale context integration; improved long-range feature extractionResearch[41]
GANsSynthetic data generation and augmentation frameworks using a generator-discriminator architectureAlgorithm development with limited data; robustness via augmentation; domain adaptation between imaging vendors; privacy-preserving data synthesisExperimental[42]
Federated learningDistributed training across institutions without centralizing patient data, using encrypted gradient updatesPrivacy protection (HIPAA/GDPR compliance); multi-institutional collaboration; improved model generalizability; larger effective training datasetsImplementation[43]
Edge computingOn-device/near-sensor computation for low-latency inference using optimized models (quantization, pruning)Real-time processing (< 100 ms); instant feedback in clinical workflow; reduced cloud dependence; enhanced data security; offline capabilityDevelopment[45]
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AI: artificial intelligence; GANs: generative adversarial networks; GDPR: General Data Protection Regulation; HIPAA: Health Insurance Portability and Accountability Act.