We can all agree that the application of Artificial Intelligence in the healthcare sector is now widespread. Of course, for good reasons, since they help shape the industry for the better. The use of Artificial Intelligence in the healthcare industry allows for easy information sharing, enhanced patient care, reduced overall costs of running the business, and many more.

Like any other technology, the use of AI in the healthcare industry also comes with its pitfalls. No wonder it pays off to recognize these challenges if you are to handle the adoption and implementation of health AI application effectively. In this quick guide, we will take you through some of the potential drawbacks accompanying AI application in the healthcare sector.

Data Loss

If you’ve done your homework, you might already know that Artificial Intelligence counts on data networks, explaining why the systems are easily susceptible to security risks. That’s why it is the sole responsibility of healthcare service providers to invest in cyber security to ensure their technology is safe and sustainable at all times.

Considering the vast amount of data stored within healthcare systems, it is easy to see why they are the perfect target for cyberattacks. This explains why data security needs to be among the highest priority in all AI development projects in the healthcare industry. It is only then that you stand a better chance of keeping your data safe as a healthcare company.

Lost Personal Approach

Health AI applications continue changing how patients interact with healthcare providers. Physicians, nurses, and other medical practitioners truly care about their patients. For this reason, there is a concern that AI implementation in the healthcare industry will tamper with face-to-face time.

Now more than ever, doctors make do with limited appointments that hinder them from picking up on their patients’ body and verbal cues. On the other hand, health AI applications leave doctors with more time to focus on interacting with patients as they handle the diagnosis part. No wonder it deserves a second thought as part of current digital transformation for healthcare.