So, where do we stand?
Revolutionary progress has been made in AI over the past 5 to 10 years. The increase in available data, significant improvements in computational power and better algorithms have made it feasible to implement AI for use cases not possible before. Clearly, healthcare, medical research and drug discovery are all very interesting fields for applying this type of AI solution. They have the potential to lessen the cognitive load on doctors, nurses and researchers, speed up pharmaceutical development and provide a much broader data-driven foundation for making diagnoses. However, there are also a number of challenges involved in leveraging these new and rather raw technologies. These include the ability of managing data without sacrificing safety and privacy, a lack of up-to-date regulations, problems related to model transparency and explainable predictions, and significant engineering overheads in putting these techniques into production.
We should, however, be clear that we are only at the very beginning of this development, and we must be careful not to read too much into press releases hyping the latest achievement. Instead, we should read with a critical eye, and always keep in mind how AI techniques can be used by people, and together with people. Specifically, we believe that the rationale for predictions made by algorithms needs to be presented in a way that builds medical practitioner trust in order to incorporate them into the decision-making process.
In the short term, radiology applications, such as image analysis and auxiliary diagnostics-related products, are likely to remain the most promising fields for AI innovations. Another type of application that could take off soon is automatic summarization and speech-to-text conversion of medical case histories. A number of companies are working on structured, automatized medical history-taking, which will open up new possibilities for machine learning to model disease progression and interdependencies between medical conditions.