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<p>The rapid advancement of intelligent technologies has driven a transformative shift in healthcare, giving rise to Healthcare 5.0, a new paradigm centered on patient-focused, digitally empowered medical services. This study presents a comprehensive review of cutting-edge Deep Learning (DL) techniques that are redefining Healthcare 5.0 through applications in disease prediction and early diagnosis, medical image analysis and radiology, and multimodal deep learning (MMDL). We analyze a broad spectrum of DL architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs), Gated Recurrent Units (GRUs), Generative Adversarial Network (GANs), transformers, autoencoders, transfer learning, and MMDL, highlighting the unique features that make them particularly suited for healthcare tasks. Using bibliometric analysis of 1,342 Scopus-indexed publications (2018-2025) via VOSviewer, we uncover key research trends, thematic clusters, and emerging focus areas. Furthermore, we assess DL models across diverse real-world healthcare datasets and discuss critical challenges, including data privacy, model interpretability, and system integration. Following the PRISMA methodology, this review also synthesizes recent research on advanced DL architectures for disease diagnosis and Healthcare 5.0, alongside widely used medical imaging and multimodal datasets. The manuscript has been substantially strengthened by incorporating a comparative analysis, quantitative aggregation of reported results, an explicit novelty and contribution statement, and a systematic comparison of DL survey studies across Healthcare 4.0 and Healthcare 5.0, thereby providing a more critical, data-driven, and clearly differentiated perspective beyond prior reviews. Finally, we address significant issues, including ethical considerations, privacy concerns, and model limitations, while outlining promising directions for future research. This work serves as a valuable resource for researchers and practitioners, offering both a broad overview and deep insights into the latest developments in DL for Healthcare 5.0 systems.</p>

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Empowering healthcare 5.0 with deep learning: techniques, trends, and future directions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月25日

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  • Paramita Kundu Maji,
  • Sanjay Chakraborty,
  • Afifa Sadiq,
  • Saikat Basu,
  • Krishnendu Ghosh

摘要

The rapid advancement of intelligent technologies has driven a transformative shift in healthcare, giving rise to Healthcare 5.0, a new paradigm centered on patient-focused, digitally empowered medical services. This study presents a comprehensive review of cutting-edge Deep Learning (DL) techniques that are redefining Healthcare 5.0 through applications in disease prediction and early diagnosis, medical image analysis and radiology, and multimodal deep learning (MMDL). We analyze a broad spectrum of DL architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs), Gated Recurrent Units (GRUs), Generative Adversarial Network (GANs), transformers, autoencoders, transfer learning, and MMDL, highlighting the unique features that make them particularly suited for healthcare tasks. Using bibliometric analysis of 1,342 Scopus-indexed publications (2018-2025) via VOSviewer, we uncover key research trends, thematic clusters, and emerging focus areas. Furthermore, we assess DL models across diverse real-world healthcare datasets and discuss critical challenges, including data privacy, model interpretability, and system integration. Following the PRISMA methodology, this review also synthesizes recent research on advanced DL architectures for disease diagnosis and Healthcare 5.0, alongside widely used medical imaging and multimodal datasets. The manuscript has been substantially strengthened by incorporating a comparative analysis, quantitative aggregation of reported results, an explicit novelty and contribution statement, and a systematic comparison of DL survey studies across Healthcare 4.0 and Healthcare 5.0, thereby providing a more critical, data-driven, and clearly differentiated perspective beyond prior reviews. Finally, we address significant issues, including ethical considerations, privacy concerns, and model limitations, while outlining promising directions for future research. This work serves as a valuable resource for researchers and practitioners, offering both a broad overview and deep insights into the latest developments in DL for Healthcare 5.0 systems.