Medical staff has many clinical challenges and developers have many solutions, if you put the two of them together, you've got a great opportunity for innovation. In this article, we describe how modern AI technologies help leverage the experience and skills of doctors to help more patients, detect diseases at earlier stages and set more correct diagnoses.

An Artificial Intelligence platform is a system created to perform more accurately and logically than regular technical frameworks. In medical imaging, AI technologies can be used to detect cancer, cardiovascular diseases, respiratory diseases, retinal diseases and neurodegenerative diseases. Machine learning-based approaches allow doctors to detect abnormalities in imaging data and adjust or accelerate the treatment.

Such platforms are created by Data Scientists and Medical Subject Matter Experts who train the machine learning models, create algorithms and pipelines to analyze 2D and 3D images made with various types of medical equipment.

The essential part of a Medical Imaging AI Platform is a dataset, which is a universal unit allowing engineers to communicate properly with subject matter experts (i.e., doctors). A dataset contains thousands of images of the same condition or pathology in as many variations as possible. It should contain all known cases and forms of the pathology, as well as marginal cases.

IDTechEx predicts the growth of AI-powered image-based medical diagnostics. In value terms, AI-enabled image-based medical diagnostics will reach $3 billion by 2030 across five segments including cancer and CVD as well as respiratory, retinal and neurodegenerative diseases.

In terms of trends, AI technology will be developing to match and exceed the abilities of a human in accuracy, sensitivity, and specificity. The technical threshold for the automation of diagnostic tasks has already been reached, which lays the foundation for commercial growth in the short and long term.

With more and more patient data accumulating, there will be more opportunities to create better datasets that would be more and more accurate in detecting abnormalities. This will require increasing the computational resources, so medical organizations will tend to use cloud solutions except for those who prefer to keep their data inside. They will be using their own data centers while increasing their capacities.

Companies will need to make a strong focus on data acquisition policies at the heart of the product development. Partnerships and customer relations will be equally important. This will protect their business, acting as a barrier to entry for newcomers.

There is no unified solution that would fit all purposes. The specific product development workflow depends on the organization, the problem that needs to be solved and the goal of the project. However, we can define some stages that each machine learning project will undergo.

At present, many diseases can be identified at an early stage with the help of Artificial Intelligence, helping to start earlier treatment. This means a better quality of life for patients and saving numerous lives. The whole medical sphere is undergoing digital transformation at an unprecedented speed. The tendency is to exclude highly qualified personnel from the process of routine screening of diseases and setting diagnoses. This serves to the benefit of both doctors and patients: the time and resources of doctors are freed in favour of actually treating patients.

The trend is to raise the AI complexity beyond trivial object detection. Today, ML algorithms match the abilities of humans, but they already can complete the same tasks faster and/or better. In the future, with more digitization of patient data, we can expect more data fusion, which will probably enable AI to offer insights beyond human capability. This could be a game-changer.