The study focuses on noninvasive diagnostic methods. Noninvasive diagnostic methods are limited in their sensitivity, specificity, and timeliness. Artificial intelligence and machine learning can bridge these gaps by finding patterns in data streams. AI was used to assist with medical imaging, such as MRI and CT scans; wearable sensors that detect electrocardiography (ECG), photoplethysmography (PPG), blood oxygen saturation, and blood pressure; and to analyze biofluids such as urine, sweat, and blood. AI usage in diagnostics requires cooperation from regulation, clinical usage, and ethical standards. There is a privacy concern about AI and ML in diagnostics. Some solutions have been proposed to address these concerns, but they are still in the early stages. Overarching standards across the medical field are needed to safeguard privacy. Additionally, bias may exist in the datasets, as the AI models are proportionally reliable to the data used to train them.