The healthcare industry can benefit immensely from the application of data sharing and AI technology. However, some experts have raised concerns about how it can be detrimental to patient privacy.
In this blog, we look at some of the security and privacy issues that have raised eyebrows for many health professionals.
The Advantages of AI and Data Sharing in Healthcare
Our medical and healthcare practices rely on tests, diagnosis, and results from the analysis that helps us come up with effective cures and procedures for patients. Research shows that no one does this job better than AI and data sharing networks.
AI-controlled machines are capable of making precise measurements for patients which allows doctors to diagnose problems at an earlier stage. Data sharing platforms allow comparison of results with other similar cases to help us identify patterns of disease spread in humans.
Many people believe that AI usage in medical practice is something new, but the technology has been around for over half a century. It is just taking a more central stage now, which doesn’t sit well with many health professionals.
Issues of Patient Privacy
It can be easy to dismiss doctors’ and healthcare professionals’ concerns about AI and patient privacy as a self-preservation strategy. If AI proves more efficient than human doctors at saving lives, they would soon find themselves at the unemployment office.
However, there is some truth to their claims that must be considered.
- Unlike humans, AI is not bound by human ethics and codes of confidentiality. It is based on computational formulas with the sole objective of improving results.
- The more data processed by the AI, the better it becomes at diagnosing disorders and recommending solutions.
- Many patients are reluctant to have their diagnostic test results shared, for one reason or another. AI tools, on the other hand, enhance their understanding of disorders by processing reports from thousands of patients.
- If we limit the access of AI solution providers to patient’s reports, the process can be pushed back decades and also make it inefficient.
- On the other hand, if we allow data sharing between AI developers, patients may sue healthcare facilities and research companies for breach of their privacy.
Solutions to Maintain Patient Privacy
There are several ways to tackle the privacy issue in AI-based medical research. One way to do it is through data encryption. This method ensures that the collected data is accurate but keeps others from identifying the test subjects.
Differential privacy is another practical solution. It introduces randomness into aggregated data, which reduces the risk of identification. It still preserves the original data and allows the AI to make accurate conclusions.
A more advanced solution called homomorphic encryption allows private medical data to be encrypted and even processed without requiring decryption by the end-user. This method is by far, the most effective and guarantees patient privacy. However, it is still at an early stage of development and requires more research.
The effectiveness of AI and data sharing in healthcare is universally accepted. However, there are many genuine security and privacy concerns from patients. Developers have started working on solutions to keep patient data confidential while sharing it between multiple parties.
A breakthrough in technology would make all the stakeholders happy and improve the efficiency of our healthcare system.