In 2023, Artificial Intelligence (AI) has been attracting our attention more than ever before. AI is a vast area of computer science that deals with creating intelligent machines that can perform tasks that typically require human intelligence. This field boasts various applications, such as automated interfaces for visual perception, speech recognition, decision-making, and language translation. Lately, AI has been involved in all kinds of fields and industries, be it science, art, or anything in between. However, it’s often healthcare and healthcare IT solutions that are quoted as a primary field where AI is revolutionizing everything from drug discovery to treatment.
Indeed, the advancement of AI has opened new doors for innovation in healthcare. But while some predict that in the future we’ll be treated almost exclusively by robots in white coats, at the moment, AI has several major clinical applications.
AI is being used to develop algorithms for medical diagnosis, drug discovery, and treatment recommendations. In medical diagnosis, AI is mostly used for its image recognition features, which, for example, identify cancer cells on an MRI or X-ray. Additionally, AI can be used to analyze patient data and medical records to identify patterns and predict patient outcomes. This AI’s ability to analyze data and draw conclusions is used to also develop personalized treatment plans. Other clinical applications of AI include medical chatbots and virtual assistants, which can provide patients with advice and support.
Finally, there is training and educating medical professionals. The use of AI in the medical training industry is still very new, and while there are some ways in which AI enhances existing healthcare IT solutions, the great times of the use of AI in medical education are undoubtedly still ahead. But let’s look at what we already have.
How is AI used in healthcare education?
The most common way that AI is being used to train medical professionals at the moment is through improving simulation technology that is used to train medical professionals. Simulation allows future doctors and nurses to practice various procedures and treatments in a safe and controlled environment. This helps them develop their skills and gain confidence before working with actual patients. Simulation technology has been used in medical training since the late 1990s and has shown various benefits in different areas of healthcare, from surgery to anesthesia. AI is used to create more realistic simulations and provide feedback and performance assessment.
Here are some of the solutions that use AI to improve medical training:
Osso VR: Osso VR is a virtual reality surgical training platform that is exploring the use of AI to provide adaptive learning experiences. The platform uses AI algorithms to analyze the trainee’s performance, provide feedback on areas for improvement, and personalize training.
FundamentalVR: FundamentalVR is a surgical training platform that uses haptic feedback and AI to create realistic simulations. The platform uses AI algorithms to adapt the simulation based on the trainee’s performance, providing a more personalized training experience.
CAE Healthcare: CAE Healthcare is a medical simulation company that is exploring the use of AI to improve simulation scenarios. The company is developing AI algorithms to generate more realistic patient responses and provide more personalized training experiences.
Osmosis: Osmosis is an online medical education platform that is exploring the use of AI to personalize learning experiences. The platform uses AI algorithms to analyze the trainee’s knowledge level and provide customized learning paths.
Health Scholars: Health Scholars is a medical training platform that uses VR and AI to create realistic simulation scenarios. The platform uses AI algorithms to adapt the scenario based on the trainee’s performance, providing more realistic and challenging scenarios.
These companies are using AI to improve medical training by providing more realistic simulations, adaptive learning experiences, and personalized feedback. As AI technology continues to evolve, we can expect to see more companies like these exploring AI’s potential in medical training.
What is the future of AI in healthcare education?
As discussed before, for the time being, companies are mostly probing the ways in which AI can help to provide personalized learning experiences to medical professionals using simulation technology and adapt to the individual needs of learners.
However, there are also ways outside of simulation technology in which AI could be helpful in the future of healthcare education.
Improve curriculum
AI can analyze data from medical training programs to identify areas for improvement and predict future trends. This could help to improve the quality of medical education and ensure that training programs are keeping pace with advances in medical technology and best practices.
Personalized learning
AI algorithms can analyze the student’s knowledge level and learning style to provide customized learning paths. This could help to improve learning outcomes and reduce the time needed to achieve competency. This could be very advantageous, as medical training is one of the longest and most costly all over the world.
Remote learning
AI-powered virtual assistants and chatbots can provide remote support for medical students and professionals, answering questions and providing guidance when needed. This could help to improve access to medical education and provide more flexible learning options. In the long term, it could involve more people in medical education and, therefore, in medical practice. With the shortage of medical staff that the world is experiencing at the moment, the importance of this is hard to overestimate.
What are the risks of using AI in medical education?
We’ve talked extensively about the benefits of involving AI in medical education. However, as usually happens, there are also potential threats to using AI in educating future doctors and nurses.
At the moment, there are vast concerns about AI technology’s potential biases and limitations. AI algorithms rely on large amounts of data to learn, and if this data is biased or incomplete, it can lead to biased results. For example, if AI algorithms are trained on a dataset that is predominantly male, they may not perform as well when evaluating female patients.
There is also an issue of transparency. Some AI algorithms are considered “black boxes,” meaning that it is difficult to understand how they arrived at a particular conclusion. This lack of transparency can make it difficult to identify and address biases.
There is also a risk that relying too heavily on AI could lead to a reduction in the hands-on experience and critical thinking skills that are essential for medical professionals. Overreliance on technology can lead to increased confidence during the decision-making process ― this is called automation bias. A systematic review from 2017 found numerous examples of automation bias in health care. Trusting AI systems may be an intractable human bias, which both medical students and their teachers should be aware of.
As often happens, there are also ethical concerns around the use of AI in medical education, particularly around privacy and data security. There are possible data breaches and other concerns associated with providing AI with tons of personal information. There is also the potential for AI to be discriminatory, and it’s important that students don’t base their experience and acquire their knowledge based on that.
As such, it is important to strike a balance between the use of AI and traditional teaching methods and to ensure that medical professionals receive a well-rounded education that incorporates both approaches. It is also important to address the biases and limitations we’ve talked about before. Constant assessment and reflection are required when working with technology when the stakes are that high.
Conclusion
Currently, the role of AI in medical education is substantial, although not as impressive and mind-blowing as the current role of AI in clinical practice. The future, however, looks decidedly promising. We can safely expect the expansion and the growth of AI in various technologies used in education and in the way it might transform medical education itself. Yet, as with anything to do with education, the process is expected to be slow and in need of constant assessment and re-assessment. As much as we value technological progress, it’s important to not rely on it too much and make sure medical professionals are able to make decisions, think critically, evaluate, and predict outcomes to the best of their ability.
