In addition to the algorithm itself, which can perform analysis with a high degree of accuracy, the success of innovations requires a successful project team. Such projects in medicine depend on how productively the participants interact.
The team should include specialists with a wide range of competencies in the domain area, mathematical algorithms, and approaches to information protection, programming skills, and visual data presentation. It is highly desirable that the participants possess not one but several different competencies in order to understand and complete each other.
The second limitation of the proposed innovations in medicine is the lack of transparency in the decision-making process when it comes to the intellectual core of the system. AI works using the black box principle. If there is an error in the algorithm, and the system has made the wrong decision, then it will be extremely difficult to find out why.
The complexity of approaches to data processing based on artificial intelligence poses another problem. We need to select and train personnel capable of efficiently using and maintaining systems with non-trivial algorithms.
Emerging difficulties increase the cost of development, implementation, and introduction of solutions based on artificial intelligence. The high cost of projects is also associated with the need to configure a new system for the data accumulated in a particular medical institution, and to construct a qualified and motivated team.
This, in turn, casts doubt on the possibility of rapidly scaling up the technologies offered by startups. Scaling is possible, for example, in the case of processing medical images of the same type, but needs go far beyond these limits.
Industry experts agree: in the short term, the introduction of artificial intelligence will not lead to a significant reduction in costs. We must continue to look for areas where the use of artificial intelligence technologies will bring higher value.
We must not forget that in order to ensure the functioning of artificial intelligence, it is necessary to provide access to high-performance computing capacities, which medical institutions often lack. Accordingly, data arrays will have to be displayed outside the perimeter of the institution, and this threatens the safety of storage, which should be the first priority. It is no coincidence that many artificial intelligence implementation projects were stopped due to risks related specifically to information security.
The expert community is fully aware of the existing problems and is trying to respond to them: specialists think through the principles of designing robotic systems, propose to discuss ethics of the use of AI in practice, and develop new options for introducing technologies. But let’s say it bluntly: it takes time to adequately work out approaches and standards.
Most of the issues that slow down the implementation of AI-based solutions in medicine are not at all related to the technological state of affairs. Most often these are ethical and administrative process problems, difficulties in collecting and processing data (e. g. providing the doctor with the time necessary to enter information into the database), problems of responsibilities, understanding mechanisms of artificial systems, etc.
Will intelligent robots replace doctors in the future? It is still a difficult question to answer. Most medical startups are aimed only at simplifying and optimizing the medical flow process, and they do not provide for the replacement of a doctor with a robot. In addition, machine learning takes time, because we need to teach the neural network to “see” the problem in the image or to operate as a doctor.
Despite all the difficulties, AI-driven medical projects have prospects. We believe that in the next couple of years, artificial intelligence will be able to find its followers among pharmaceutical companies. It will be of good use in the search for new molecules and biological targets, in the virtualization of preclinical trials, and then in the analysis of clinical research data. When working with large companies, it is often possible to provide all the conditions necessary for a successful project launch.
All issues can be resolved. The main thing is that with the use of new approaches we should be able to move forward, and not cause greater harm.