Potential Challenges & Problems with AI in Healthcare Technology

Many additional benefits have been discovered since the advent and integration of artificial intelligence (AI) into today’s biomedical devices and equipment. For instance, AI algorithms have already been proven to detect certain health conditions in patient images and extract important information from large volumes of unorganized patient electronic health records (EHR) better than humans. Thus, as more potential applications have presented themselves in the field of healthcare, the drive to further advance AI as a technology continues. However, with all these potential benefits AI presents, new challenges and problems arise that biomedical technologists and engineers must overcome as well. 

The first of these challenges in implementing AI into healthcare technology is the increased vulnerability to data breaches. Since AI requires direct access to healthcare data containing sensitive patient information, thorough cybersecurity measures must be implemented to ensure that attackers cannot take advantage of new network access points. Otherwise, failure to properly implement a robust cybersecurity protocol can lead to serious ethical and legal consequences for any healthcare facility. Furthermore, it is also important that AI models utilized in today’s healthcare technology continue to comply with all relevant regulations, acts, and standards when handling sensitive patient data. This means that it’s the responsibility of biomedical technologists, and engineers to be aware of this vulnerability that AI-integrated medical devices, equipment, and systems present when they’re connected to any healthcare network. 

Another problem that could present itself is AI models not being trained on large enough amounts of high-quality healthcare data, compromising the reliability, and integrity of the decisions being made that are intended to help medical professionals perform their necessary workloads. The reason this would be a challenge is that countries have their own rules and regulations about data privacy, which could limit the amount of available data for training specific AI healthcare models. Not only is the amount of available data an important factor, but the quality of that data is also just as important, especially when collecting and selecting healthcare data to train AI models. The reason why quality becomes an issue is that healthcare data in the form of EHRs are often disorganized and contain missing information that is essential in making medical decisions. Although certain data preprocessing steps and practices can be applied to accommodate for these quantity and quality issues to ensure training of AI models is performed more effectively, they are not enough to meet the standards required in the field of healthcare. Therefore, biomedical technologists and engineers must work together to help make the digitizing of healthcare data more mainstream and common practice across all healthcare organizations worldwide. In doing so, this will help over time in improving the current quality and quantity of patient data being used to train AI models used in healthcare technology. Lastly, as for the challenge of gaining access to patient data due to privacy regulations, biomedical technologists and engineers must work with data scientists to find an alternative means of continuing AI healthcare model development. 

To add on, a third problem with implementing AI into the design of medical devices, equipment, and systems is the “grey area” when it comes to who’s to be held liable when adverse events occur. Given the serious legal consequences that could be faced, there needs to be standards and guidelines that will regulate the use of AI as they’re used in making critical healthcare decisions and or assisting medical professionals in improving patient outcomes. However, as of writing this article, organizations such as the Food Drug Administration (FDA) and the National Health Service (NHS) are still currently developing these needed standards and guidelines. Eventually, when these standards and regulations can be officially adopted, it will make the submission process for taking AI healthcare technology to the market much easier. However, as more progress is being made in establishing these AI healthcare regulations, the more challenging it becomes for legal system decisions to be made on whether further development permissions should be granted or not. Thus, to mitigate these legal challenges, healthcare technology standards and regulations should not be written in a way that hinders AI’s integration into medical devices, equipment, and systems. To accomplish this, biomedical technologists and engineers should be involved in the drafting work of all relevant healthcare standardization and regulation organizations. This is because they possess the necessary knowledge and expertise required on how to ensure AI healthcare standards and regulations can still serve their purposes while not limiting further technological advancement. 

In conclusion, there were only three potential problems discussed in this blog post, but there are even more challenges that are currently challenging the further implementation of AI to advance healthcare technology. Other potential challenges and problems include the inability to understand the decision-making processes of AI models and the possibility that AI may replace the existing roles of current healthcare workers to name a few more. Regardless, AI has the potential to revolutionize patient care and improve medical outcomes with many applications having been discovered in such a short period. With that in mind, biomedical technologists and engineers must collaborate to properly implement AI technology as part of medical devices, equipment, and systems without compromising their existing functionality. They must take a slow, systematic approach to guarantee that patients, other healthcare staff, and any relevant others are protected from the adverse events that may occur while still harnessing its amazing benefits.