Data Privacy and AI in Healthcare

Artificial intelligence (AI) has the potential to significantly improve efficiency in the medical field. However, as the healthcare sector has very sensitive data, organizations and regulators need to make sure that the technology they employ is safe and effective.

In healthcare, data is very personal and contains a lot of sensitive data about the patient. Making the field is particularly sensitive and risky for several reasons:

• Mistakes can cost lives: Healthcare is not a field in which mistakes are easily tolerated. Prescription and diagnostic errors are common and AI needs to be an aid to medical professionals to improve their ability to diagnose effectively.
• Privacy concerns: Using AI and machine learning has some major ethical and privacy considerations attached to it. Do people consent to doctors transferring their data to large data centers for data processing? Is it a breach of physician-patient privilege?
• Data privacy: Data security is also a vital concern when using AI and data science in healthcare. Healthcare documents and data can contain very sensitive and personal information. Making sure the process is anonymized and secure is very important.

How AI can improve the healthcare industry

AI in healthcare can solve the many issues the industry faces such as healthcare worker shortages, health disparities, age-related diseases and more. The topic is increasingly becoming a major research point for top universities such as the TU Delft.

In general, AI can outperform humans in tasks such as pattern recognition, making them useful tools for diagnostics. While there is still a lot of work to be done and some limitations, it is a technology which can potentially improve the healthcare system substantially.

Remaining Issues

While AI in healthcare has a lot of potential and is currently effective, there are still some major challenges which remain. (with regards to data privacy)

Data privacy and ethical considerations

Data privacy is a vital concern within the healthcare industry. Processing medical files and records needs to be done in both an anonymous and well-regulated way. The collection of data for advertising and informational purposes by third parties without proper consent poses a significant challenge within the big data industry.

Data reuse is a term which describes the usage of data for purposes other than what the main parties agreed to. Meaning that clinical data can be stored, collected and used for other purposes than purely for direct medical reasons. In the medical field, this is particularly important as it is a breach of medical ethics and of physician-patient privilege.

NOS warned against the use of chatbots at workplaces, explicitly because they often use data for training purposes, they could potentially store sensitive company and patient information. In the healthcare field, this could lead to risks for patients.

3rd party data usage is when data is given or accessed by third parties for reasons outside of what the patient and physician intended. If the data is shared to third parties, it can easily be spread and stored for various purposes. If data is shared at all, it needs to be done with formal consent and anonymized.

MIT Sloan released an article detailing the potential dangers third party AI tools pose to organizations. They show how it could lead to lack of trust, regulatory issues and even lawsuits. It is important for companies, especially in the healthcare industry, to make sure the data is transferred to as few parties as possible and to make sure it stays within a secure pipeline.

Data security

Having flawed and vulnerable data pipelines can have disastrous consequences, especially when the data contains sensitive personal information. Organizations need to make sure that their data pipelines are secure from attacks and leaks. According to an article released on Coalfire, “The loss of confidentiality of sensitive or personally identifiable data (PII) is probably the most well-known, well-documented, and impactful risk [of AI]”.

Along with standard social engineering and vulnerability exploitations, there are several ways malicious actors can take advantage of AI systems specifically. Washington state university released a list of ways AI models are vulnerable to malicious actors such as Adversarial Attacks and Model Poisoning.

If you are interested in learning more about exploiting chatbot vulnerabilities and malware generation in general, the IEEE released a report surrounding chatgpt and its malware potential.

Compliance with regulations

Recent regulatory advances in the EU make complying with data protection rules increasingly difficult and challenging. The new EU AI act has new stringent requirements for data protection, which could potentially disincentivize investors from investing in an industry where regulations are continuously changing. Being GDPR compliant means that your entire data pipeline needs to be compliant. Meaning that storing data in servers outside of the EU is risky.

You can make sure companies are complying with data protection regulations and best practices by checking their certifications. Here is a brief description of two of the major ones:

Soc2 is a data privacy certification which makes sure that data is secure and private. It checks for vulnerabilities such as man-in-the-middle attacks, formal consent, regulatory compliance, secure exchanges and more.

Nen7510 is a Dutch data privacy certificate which all organizations dealing with medical data need to comply with. It essentially checks how secure and well managed data is and is fairly comprehensive. It also requires regular independent verifications.

Conclusion

To summarize, AI in healthcare can be used and is already being used effectively. However, major challenges still remain. Given how sensitive healthcare data is, data privacy and protection from breaches and third-party use is essential.

At UbiOps, we are compliant with multiple comprehensive certifications including SOC2 and Nen7510. We also offer the ability to deploy models on-premise, meaning that data does not leave your servers, making third party usage and leaks a minimal problem. In general, storing data and models on-premise can be safer than having to trust third parties and cloud providers. At UbiOps we offer the ability for you to deploy on-premise or hybrid.

Hybrid cloud deployment is becoming increasingly popular for many organizations. For data sensitive industries like healthcare, an option would be to keep highly sensitive and production data on-premise and use training or experimental data on cloud. This setup is becoming increasingly popular.

In general, data protection, security and regulatory compliance are going to be an increasingly important issue that organizations will have to deal with. If you are interested in learning more about UbiOps, visit our healthcare page. We work with Ellogon AI and collaborate with healthcare organizations, including hospitals and companies. Contact us if you have any questions or are interested in our platform.