Table 1.

Overview of advanced deep learning models in healthcare diagnosis and prognosis.

Algorithm Type	General Application	Limitations	Comments	Example
Convolutional Neural Networks (CNNs)	Image recognition and analysis in medical imaging (e.g., X-rays, MRI, CT scans)	Require large labeled datasets and substantial computational resources; can be a “black box” making interpretability difficult	Highly effective for spatial data; state of the art in medical image analysis	Deeplab v3+, a CNN variant for gastric cancer segmentation [28]. Results: 95.76% accuracy, outperforming SegNet/ICNet.
Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks	Analysis of sequential data such as ECG, EEG signals, or patient health records	Prone to overfitting on smaller datasets; long training times; difficulty in parallelizing the tasks	Suited for time-series data; LSTM addresses vanishing gradient problem in RNNs	LSTM for EEG signal classification [29]. Results: 71.3% accuracy, utilizing novel one-dimensional gradient descent activation functions for enhanced performance.
Transformer Models (e.g., BERT, GPT)	Natural language processing tasks, including clinical text analysis and patient history summarization	Require significant computational power and memory; pre-training on large datasets is time-consuming	Offer state-of-the-art performance in NLP; enable understanding of context in clinical documentation	Clinical-specific BERT (Transformer) for Japanese text analysis [30]: pre-trained on 120 million texts, achieving 0.773 Masked-LM and 0.975 Next Sentence Prediction accuracy, indicating potential for complex medical NLP tasks.
Generative Adversarial Networks (GANs)	Synthetic data generation for training models without compromising patient privacy; augmenting datasets	Training stability issues; generating high-quality data is challenging	Useful in data-limited scenarios; potential in creating realistic medical images for training	Differentially private GAN for synthetic data generation: utilizes convolutional AEs and GANs to produce realistic synthetic medical data, preserving data characteristics and outperforming existing models [31].
Graph Neural Networks (GNNs)	Modeling complex relationships and interactions between health data points (e.g., drug interaction prediction, disease progression modeling)	Complex model architectures that are difficult to interpret; scalability to very large graphs	Effective for data represented as graphs; emerging applications in personalized medicine	Knowledge-GNN for drug–drug interaction prediction: leverages knowledge graphs to capture complex drug relationships and neighborhood information, outperforming conventional models [32].