Survey on Healthcare and Disease Diagnosis via AI
DOI:
https://doi.org/10.21070/jicte.v10i1.1724Keywords:
Artificial Intelligence, Machine Learning, Disease Diagnosis, Medical Imaging, Deep LearningAbstract
General Background: Modern healthcare relies heavily on accurate disease diagnosis to analyze complex patient data and select effective treatment plans. Specific Background: Advanced computational models, particularly machine learning and deep learning algorithms, are increasingly integrated into clinical settings to interpret medical images, biosignals, and genomic datasets. Knowledge Gap: Existing literature lacks a consolidated overview connecting foundational communication and decision theories directly to the practical deployment, challenges, and legal liabilities of diagnostic algorithms in clinical workflows. Aims: This survey synthesizes foundational machine learning concepts, theoretical models, and practical applications to assess current progress and operational bottlenecks in disease diagnosis. Results: Deep learning architecture demonstrates superior capability in detecting subtle lesions, cardiovascular conditions, and complex malignancies from multi-modal diagnostic data while drastically reducing processing times. Novelty: This review uniquely bridges communication and social exchange frameworks with core diagnostic neural networks to highlight human-algorithmic interaction in clinical environments. Implications: Implementing these decision-support tools mitigates diagnostic errors, streamlines hospital resource allocation, and fosters sustainable precision medicine strategies.
Key Findings Highlights
Neural network architectures substantially improve the precision and speed of identifying complex malignancies and cardiovascular conditions from clinical datasets.
Integrating theoretical communication frameworks into algorithmic models enhances multi-modal clinical decision support and healthcare data interpretation.
Data scarcity, potential algorithmic bias, and legal liability issues represent significant barriers to full operational adoption in clinical practice.
Keywords : Artificial Intelligence, Machine Learning, Disease Diagnosis, Medical Imaging, Deep Learning
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