Abstract
Background: There exists no doubt that AI and machine learning are some of the revolutionary tools in healthcare with specific emphasis on disease diagnosis. These technologies improves the diagnostic outcomes, timeliness, and accuracy by leveraging the computation techniques to analyze the medical images and patient information compared with the traditional diagnostic approaches because of the vulnerabilities in accuracy, time, and variability of the medical expertise. The daily increasing demands for precision medicine preponderate the application of AI/ML in medical diagnostics even more.
Aim: It is this article analyses the advancements that AI and ML algorithms are bringing into disease diagnosis due to enhance in analysis of medical images and patient data.
Method: The following AI/ML algorithms are discussed in the study – neural networks, deep learning, and support vector machines and details of how they are trained on large datasets is also highlighted in relation to image recognition and data patterns. It also revisits some of the major data sources; imaging studies (X-rays, MRI, CT scan) and electronic health records (EHRs).
Results: AI/ML has a higher diagnostic accuracy and performs better than the conventional one that includes cancer and diabetic retinopathy. Moreover, these algorithms have decreased the time taken to complete the processing of results and eliminated the likelihood of human error, hence offering accurate diagnosis.
Conclusion: In the long term the increase in diagnostic accuracy along with the use of AI is beneficial for patient’s care hence is signifying a significant value for the growth of healthcare in the future. Nevertheless, there are certain issues which already have appeared on the way of AI application in the task of medical diagnosis, and which should be further investigated and solved: data protection, algorithmic bias, or the lack of transparency.
Keywords: Artificial Intelligence, Machine Learning, Disease Diagnosis, Medical Imaging, Precision Medicine, Diagnostic Accuracy, Healthcare