Engineering Intelligent Decision-Support Systems for Medical Image Analysis: A Review of Computer Vision Models and Clinical Chatbots

Abstract

Deploying artificial intelligence systems for medical image analysis in clinical settings involves considerations that go beyond model accuracy: infrastructure constraints, integration with existing workflows, and generalization across patient populations all determine whether a system works outside a research lab. This paper examines how computer vision architectures and large language models can be combined into a unified decision-support system for early detection of diseases through medical image analysis. Through a systematic review of 25 recent studies published between 2022–2025, the work develops an engineering-oriented taxonomy of architectures such as VGG16, DeiT, GPT-4 and LLaMA2, evaluating models against criteria including computational complexity, scalability, dataset dependency and deployment feasibility in low-resource environments. The evidence indicates that combining automated image analysis with LLM-based decision support can improve diagnostic accuracy and lower screening costs, with classification metrics exceeding 90% in controlled settings, contributing to the fulfillment of the Sustainable Development Goals. However, real-world deployment remains constrained by hardware requirements, interoperability gaps and dataset bias that limit generalization across diverse populations. This analysis provides concrete engineering guidelines for the design, validation and scalable implementation of AI systems in medical image analysis clinical workflows.