Artificial intelligence (AI) is considered as a future disruptive technology that involves the use of computerised algorithms to dissect complicated data. Among the most promising applications of AI is in medical diagnostic and imaging, performing tasks of quantification of a wide array of clinical conditions. In the last decade, investigations leveraging AI-aided diagnostics have shown excellent accuracy, sensitivity, and specificity for the detection of small radiographic abnormalities, with the potential to improve public health. Moreover, the use of non-patient-focused radiographic and pathological endpoints might enhance the estimated sensitivity at the expense of increasing false positives and possible overdiagnosis as a result of identifying minor changes that might reflect subclinical or indolent disease. The refinement of AI imaging studies via consistent selection of clinically meaningful endpoints will contribute in the survival of patients, explain health symptoms, and will determine the right pharmacological treatment. With the help of Deep Learning (DL) algorithms, medical imaging technology now enables medical practitioners to identify abnormalities and detect diseases with a higher level of precision and speed than ever before. This has contributed to significant improvements in the accuracy of diagnosis, the efficiency of treatment, and the overall quality of patient care. AI-powered medical imaging is hoped to be in the near future a substantial improvement in diagnostic changes and applications that will bring substantial benefits for the medical practitioners. Artificial intelligence (AI) techniques in recent developments have shown the potential to accelerate the progression of diagnosis and treatment of cardiovascular diseases (CVDs), including heart failure, hypertrophic cardiomyopathy, congenital heart disease and so on. AI has been proved to apply well in CVD diagnosis, enhance effectiveness of auxiliary tools, disease stratification and typing, and outcome Breast mammograms for cancer detection, Pap tests, colon cancer imaging, brain tumour imaging are used regularly to check people for signs of cancer or precancerous cells that can turn into malignant tumours. In the last decade medical practitioners have developed AI tools to aid screening tests for several kinds of cancer. AI-based algorithms have been used to help doctors interpret mammograms for more than 20 years, but research in this area is quickly evolving with accuracy approaching more than 90%. Many applications of AI in the reading of breast mammograms, are more than 20% accurate in identifying cancers than the routine double reading by two different radiologists and did not increase false positives. This review has collected some of the most important and recent scientific articles, reviews and applications of AI-based medical diagnostic and imaging methods and the substantial achievements in accurate identification of tumours in various organs.