AI is an emerging technology that can provide value to healthcare training and education while supporting currently licensed doctors in their work. By implementing AI into the healthcare workplace and education institutions, we can improve healthcare outcomes globally whilst retaining the integrity of healthcare services. In this article, I will explain how this is possible, and the obstacles that must be navigated for successful integration.

AI provides an essential and transformative role in healthcare education and training by enhancing learning experiences, improving accessibility across country borders, and increasing learning efficiency when compared to similar traditional learning methods ¹. By providing personalised tutoring, realistic artificial scenarios through VR and AR, and customised feedback without the need for human input, AI has the potential to significantly enhance the quality of healthcare education and training, especially in hard-to-access areas. Furthermore, AI streamlines several repetitive tasks that humans may find boring or repetitive, and as such removes an aspect of human error from a workplace that requires the highest level of standards. Ultimately, by further implementing AI in conjunction with human input, we can create a more effective learning and training environment for both licensed and trained healthcare professionals, improving healthcare outcomes.

Introduction

Definition of AI

AI, also known as artificial intelligence, is simulated human intelligence coming from a computer or machine. Conventional computation can be examined as a calculator – it would take a human input and generate an output from the information provided, e.g. 2+2=4. AI is capable of drawing upon an array of information from a database and learning from experience, recognising patterns, and generating information in a way like human thought.  

The capabilities of AI are limited only by the information presented in the database provided. If the database has expert level information present within it, we can count the AI as an expert within that field. The important part of understanding AI is the limitation of creativity – while AI can generate information based upon extant information present in a system, it is incapable of creativity. However, this can be mitigated by increasing the scope of the database the AI draws upon. The AI system is only as good as the database allows it to be.

Importance of AI in modern industries

AI is most useful when used in a way that refers to data directly. This includes automation of mundane tasks that may be liable when performed by humans, analysing Big Data to find patterns that would be missed by humans, handling financial data, and recently aiding in diagnosis, treatment, planning, personalised medicine, and managing patient records in a hospital setting.

While not a replacement for tailored healthcare and support from a licensed professional, AI can be used in conjunction with a team of healthcare professionals to diagnose and treat patients. Better than this, it can be used as a pillar of healthcare education, providing tailored educational content, enabling adaptive learning, and offering tutoring systems outside of a classroom setting.

AI in healthcare education

AI-driven adaptive learning systems analyse the preferred learning styles of the student in question. Rather than aiming to teach every student in the same way, it can adapt its style to what the student lacks. This is a noted lack in many teachers, especially with increasing university sizes and decreasing tutorial times in universities. This also can lead to greater use of effective learning methods for students, and lead to better educational outcomes.

AI is also capable of generating scenarios impossible with conventional teaching and learning environments. Immersive situations within VR and AR technologies can assist students in practising clinical procedures and patient interactions in risk-free environments. Further to this, AR can be used in clinical settings, with doctors having access to a wealth of information overlaying the real world in a diagnostic setting.

AI in healthcare training

High-fidelity simulations can be used to create realistic clinical scenarios. By simulating real life clinical scenarios, it can be possible to get trainees to practice procedures outside of being a medical student or outside of theoretical practice. Crucially, this also avoids many problems present within conventional training, like racial bias, life-altering mistakes, and using up time of consultants. It also can generate scenarios that have different presentations, allowing the student to adapt to the information presented, no matter how niche the condition may be.

Additionally, this will develop the soft skills of a student, providing a safe platform for the development of the student’s bedside manner and language used in treating a patient. This would be trained suitably to include all manners of patients and families, providing the support needed for suitable treatment of a patient.

Decision support systems

AI powered decision support systems would be able to analyse patient data across multiple patients in a protected setting and provide information pertaining to the potential diagnosis of the patient. These systems use vast medical databases and analyse patterns to assist trainees in making accurate and informed decisions. From this information, a treatment plan would be recommended based on the latest medical research, patient data, and published peer-reviewed studies. Rather than simply relying on the knowledge and expertise of a single medical professional, it takes evidence-based treatment to the next level, drawing upon decades of experience and expertise.

AI does not make mistakes in the same manner as humans. For this reason, AI is useful in the objective evaluation of surgical techniques, procedural accuracy, and adherence to clinical guidelines. It can then provide feedback, to a licensed professional or medical trainee, about how to improve in future scenarios. This can be extrapolated into competency training of members of the medical industry, ensuring that trainees and professionals meet the standard on a continual basis, rather than passing a single check every now and again.

Benefits of AI in healthcare education and training

Accessibility

AI allows for both online courses and virtual classrooms, rather than solely relying on physical presence learning environments. This contributes to flexible learning schedules that suit the needs of the students, rather than prioritising the institution and educators. This can be combined with stringent rules set by a given organisation to fit timelines and examinations but allows for more leniency in learning.

It also provides a solution to people not being able to access given institutions because of locations. Online learning environments ignore physical boundaries, and since AI is very proficient at translating contextually rather than word-by-word, language barriers are not difficult to overcome. As long as all parties involved have the required internet infrastructure, the educational environment and materials can be provided for little to no cost.

Efficiency

AI can handle various administrative tasks like scheduling, grading, and record keeping. As long as the auditing processes are in place to maintain a baseline of standards, the workload for educators is reduced, and productivity can be increased. This also leads to a knock-on effect of saving money on travel and physical resources, increasing proportional spending on educational resources themselves.

This in turn leads to a healthcare system that runs more efficiently on any given provision of resources. Higher quality training, especially when concerning the global south, is essential for better health equity.

AI can implement the latest healthcare data and studies into training much faster than conventional healthcare education. It continually enhances the learning experience by incorporating new algorithms, data sources, and educational methodologies. This keeps healthcare education cutting-edge and relevant.

Challenges and considerations

Ethical concerns

Data security is a common problem with modern AI systems. Health information is particularly a concern in this regard, as it can contain individually identifying health information. Ensuring the privacy and security of this data is crucial to protect individuals from breaches and misuse.

AI systems are only as unbiased as the datasets they are trained upon, and if the data are curated on biased information, the AI will be biased as a result. This is especially concerning when examining the impact of racial bias, as this has been historically relevant for healthcare outcomes ². As a result, particular caution may be necessary to prevent any dataset issues from coming about.

Implementation barriers

Implementing AI in healthcare education and training requires a significant amount of investment before the AI system will be capable of handling the workload of healthcare education and training. This would then require further long-term investment to cover maintenance, updates, software, and initial training. Institutions may find this initial and ongoing cost prohibitive.

Cultural barriers and fear of replacement could lead to issues regarding the uptake of AI systems. This can include unfamiliarity, fear of job displacement, or a preference for traditional methods. An emphasis on the benefits such systems could bring when initialised, would help to bypass this resistance to change, and instead bring the focus onto how to work with the AI systems.

Meeting the currently set industry standards may be a strong barrier to the implementation of AI in healthcare education and training settings. Rigorous testing over the course of several years would need to take place. However, due to the prevalence of AI systems in healthcare already, the barrier is partially broken down and should be easy to integrate in healthcare systems.

Summary

In conclusion, AI is transforming healthcare education and training by enhancing accessibility, efficiency, and learning outcomes. It enables remote learning, provides high-quality resources, and automates administrative tasks, making education more affordable and flexible. AI supports personalised learning, continuous professional development, and realistic simulations, improving the preparedness of healthcare professionals. Despite challenges such as data privacy, bias, and implementation costs, AI can be effectively integrated by ensuring robust security, fair data practices, and compliance with regulations. Addressing societal and cultural impacts, such as job displacement concerns and public acceptance, is crucial for maximizing AI’s benefits and improving patient care outcomes.