Evaluation of deep learning models used in healthcare

Background: Artificial intelligence (AI) is rapidly growing, and in many fields, it is anticipated that intelligent computers will either eventually replace or enhance human capabilities. AI is a general term that includes computer vision, natural language processing (NLP), machine learning (ML) models, and computers. ML is a subfield of AI that uses statistical techniques to let machines learn from their mistakes and get better over time. Deep learning, a branch of machine learning, is extensively utilized in the medical field and creates multi-layer neural networks that replicate brain activity. These networks are trained using gradient descent and backpropagation. Method. This study was a systematic review that involved a review where 23 papers were obtained from IEE, Google Scholar, Pubmed, and Science Direct. Papers spanning 2008-2025 were included. Since the Clinical Trial Number was irrelevant, it was left out of the text. Results: Convolutional neural networks (CNNs) for images, recurrent neural networks (RNNs) for sequential data, and deep belief networks (DBNs) for MRI and 3D images are examples of common deep learning models in the healthcare industry. High costs, interpretability, and susceptibility to missing data are some of the problems that CNNs and RNNs encounter. Multimodal learning and explainable AI are key components of future solutions. DBNs use unsupervised learning to find patterns without labeled data, overcoming the constraints of backpropagation. Conclusion: In the medical field, CNNs, RNNs, and DBNs are essential and commonly used. Ongoing research attempts to improve their efficacy through multimodal learning, explainable AI, and privacy-preserving technologies, despite obstacles including computational complexity, missing data, and security issues. These models will further revolutionize healthcare by enhancing patient care, diagnostics, and disease prediction as deep learning advances. However, more research is required to determine their efficacy and cost-effectiveness in the healthcare industry.