If you’ve done your homework, you probably already know about the numerous benefits that accompany health AI application. Not only does it lead to increased efficiency of the diagnostics process but it also reduces the overall costs of running the business. Better it leads to safer surgeries and enhanced patient care.
Even though we can never downplay the indisputable benefits that speak in favor of the use of AI in the healthcare sector, it is also essential that we recognize these solutions also come with several drawbacks. To ensure successful adoption and implementation of health AI application, it pays off to better understand these challenges. Below are some of the most notable ones.
Ensuring Patient Data Quality
Ensuring patient data quality is arguably a more critical challenge in the healthcare sector as it is highly personal information and lives could be at risk. For instance, Artificial Intelligence (AI) is now widely used for disease diagnosis and operates much faster than humans. Despite this, the accuracy of results entirely depends on the quality of training data.
To deal with this challenge once and for all, healthcare professionals need to collect AI data from trusted sources. Furthermore, the data ought to be diverse enough to reduce the impact of bias. Skimp on this, and health AI could lead to inequality rather than promote efficiency.
Training Users
One of the most notable hindrances to health AI application success is a lack of staff and patient education in AI tools. With predictive modeling, for instance, AI can be used to forecast the probability of hundreds of outcomes. If your solution merely targets clinicians, you should expect a sober learning curve when compared to users who aren’t accustomed to using software in their day-to-day work, such as patients or medical staff.
The Bottom Line
Even though the use of Artificial Intelligence (AI) in the healthcare sector offers numerous benefits, it also comes with its drawbacks as is the case with any other technology. That’s not to say you should steer clear from making it part of your company operations.
The catch is in learning what it takes to overcome the above and other challenges before adopting and implementing AI in the health sector. By taking this approach, you will certainly reduce the chances of making costly mistakes once you are already underway with the implementation process.
