AI related technologies are set to transform all industries in a massive way. In fact, most stakeholders believe that AI technologies will have almost the same landscape changing effects that electricity had on the entire human civilization over 100 years ago. The integration of AI into the healthcare sector is just one of the ways we’re going to feel this effect in just a few short years. However, since the healthcare and medical industry is a unique one, AI integration comes with its own set of challenges and opportunities. These will be the focus of this article.
Management and Intergration of Large Data Sets
AI technology, particularly machine learning technologies, need to be taught how to do everything. They are useless if they do not learn how to carry out their functions. To this end, they are fed datasets which they study and retrieve the information they need to perform various functions.
When it comes to the healthcare sector, there is a massive amount of data available in both structured and unstructured formats, which the AI can study and implement. However, the main challenge with this is that since the healthcare industry is very diverse and complex, compiling this data from various sources can be difficult. Healthcare providers often also have budget constraints that prevent them from properly maintaining their current technologies or investing in new ones. This is one of the main reasons why most hospitals run on outdated computers with outdated software installed.
As a solution, AI data lakes can be employed. These can flexibly collect and store large data files, making them easier to analyze and classify. This data can then be used to teach machine learning technologies. Although this process is highly automated, humans are still necessary and involved because they need to manually confirm accuracy, completeness and the overall uniformity of the data entered.
Data Accessibility
There is currently a huge need for real-time data sharing and universal accessibility. The healthcare industry runs on accessible data and sharing of information. To make it even better, there is an urgent need to have accessible and shareable information from all over the world that machine learning and AI technologies can take advantage of. This can lead to rapid curing of diseases and innovations in treatment procedures, not to mention research studies from case studies. However, data accessibility still remains a major challenge in this space, because healthcare data is viewed as highly private documentation.
On the other hand, if anything near universal information sharing and accessibility can be achieved, a massively complex and rapid performance AI system would need to be introduced to sift through the data, categorizing it and sorting what needs to be implemented and what does not. There will be the need to share patient and medical data in real time to maximize its efficiency.
Data Security and Patient Privacy
AI technology can have a huge impact on reinstating data security and patient privacy in the healthcare sector. This is actually one of the main concerns of stakeholders who are hesitant about the introduction and implementation of large datasets and data lakes. Plus, political and legal limitations means that it is difficult to achieve this today.
Some of the main reasons why there is resistance in this field include the fact that most data collection practices are ambiguous. There is also a lack of knowledge and information about how this data will be used and reused through machine learning. Finally, there is little to no accountability for automated decision making, which can be a problem for many people in positions where such automated decisions can be problematic if they go south.
In truth, there are no actual solutions to these issues. It is definitely going to take some time, but the great thing is that there are some healthy and hopefully productive debated going on.
Individuality vs Mass Data
People are different. This is especially true when it comes to the health sector. One person’s reactions to different medicines or even general disease symptoms may not always be the same as that of another. This can be problematic for the rigid styles that machine learning systems take advantage of. The challenge of learning from imperfect data and being expected to produce perfect results cannot be ignored.
As a solution, individual-based machine learning processes have been proposed. For example, the use of hearing devices can be studied at an individual level and an individual solution proposed through the use of AI technology because different people suffer from different hearing conditions and different levels of hearing loss. However, as with most pioneering technological solutions, these can be prohibitively expensive, time-consuming and the technology itself is definitely in its infancy.