Utilizing Artificial Intelligence in Digestive and Respiratory Disease Diagnosis

Machine learning in particular has been identified as a popular technique in the diagnostic process of diseases of the digestive and respiratory tracts. Therefore, the focus of this paper will be to establish the extent to which AI has advanced in diagnosing digestive diseases through possibility, limitation and future potential implications in the health sector. The emphasis is made on the questions of which further developments of the ML/DL have the highest potential of increased diagnostic accuracy, stability on new datasets, and adaptability to clinical practice and what complications did the models encounter and which have to be solved. Based on the analysis of the selected works, the conclusion is made that AI, including Deep Learning algorithms such as CNN, have demonstrated a high degree of potential regarding the disease diagnosis field. It emphasizes how such models work well in handling large and messy data, an aspect prevalent in the health sector. Its capacity to revolutionize the diagnostic landscape due to high precision and ability to detect barely visible lesions during endoscopy for early disease identification bear testimony to this aspect of the model's revolutionary possibilities. Further, in more studies, researchers ought to focus on enhancing the precision, mechanisms behind, multi data compatibilities, validations and examinations of such algorithms like CNNs, LASSOs, ANNs as well as acknowledging real-world implications to enhance the actualism of these models in medical diagnosis.