Artificial Intelligence in Medical Image Diagnosis: Advances, Challenges, and Future Perspectives

Abstract

This review examines the advances, challenges, and future directions of artificial intelligence (AI) in medical image diagnosis. Medical image diagnosis is vital for modern healthcare but faces bottlenecks like heavy workloads and potential human errors. AI, especially deep learning, has driven transformative progress: U-Net-based models excel in medical image segmentation (e.g., multimodal imaging for soft tissue sarcoma); CNNs achieve high accuracy in disease detection (e.g., ~96.57% for TB in chest X-rays, 99.75% for brain tumor MRI); GANs generate synthetic data and enhance images (e.g., AM-CGAN for chest X-rays), with denoising diffusion models outperforming GANs in diversity/fidelity; Transformers (e.g., TransUNet) capture global features to improve segmentation. AI applications span modalities: chest X-rays for COVID-19 (sensitivity 94.7%), MRI for brain tumors, CT for cardiovascular assessment, ultrasound for breast cancer, and retinal imaging for diabetic retinopathy. However, challenges persist: data bias affecting generalizability, "black-box" AI lacking interpretability, regulatory/ethical issues, and data privacy concerns. Future trends include federated learning for collaborative, privacy-preserving model training, AI-powered radiomics for personalized medicine, AI integration into clinical workflows, and self-supervised learning to address limited labeled data. AI holds great promise for advancing precision healthcare and improving patient outcomes.

Keywords

artificial intelligence / medical imaging / medical diagnosis / deep learning

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