Artificial intelligence applications are massively used in most of today’s healthcare systems, from medical diagnostics to e-health assistants to robot-assisted surgeries. This has leveraged the efficiency of healthcare professionals and helped patients as well. However, these advantages may come at the cost of security of patient data and professionals

The healthcare sector is one of the most vulnerable in industries when it comes to cybersecurity. This vulnerability has significantly increase since the starting of the Covid-19 pandemic.

The intensity of healthcare-related incidents, Artificial intelligence (AI) applications and cybersecurity threats in healthcare are currently facing severe attacks. Cyberattacks have become more advanced using AI, therefore attacking systems that are secured with conventional methods becomes easier.

It is thus important that AI applications in healthcare have a high consideration while developing new AI- algorithm for security to constantly manage and secure the increasing volume of healthcare Internet of Things (IoT) sensor nodes as they connect and disconnect from healthcare networks. New AI techniques are expected to enhance cybersecurity by assisting human system managers with automated monitoring, analysis, and responses to adversarial attacks

The importance of medical data security

Medical data contains sensitive personal information, and most of patients aren't willing to share that data with anyone making medical data more sensitive. Patient data in healthcare includes a lot of personal and financial information such as an individual’s social security number, and payment details like, credit cards and insurance.

Risks and Threats

The AI-enabled healthcare space deals with a huge amount of medical data which is considered as treasure trove by hackers and is frequently under cyberattacks. Since individuals from different departments might require access to electronic health records, they are mostly web-based making them vulnerable to these attacks. Additionally, the use of legacy software and outdated IT security policies can leave systems vulnerable to external attackers. External threats are not the only concern, sometimes negligent or untrained staff can cause a data breach unknowingly.

Data Sharing

With artificial intelligence in use, there are many scenarios where clinical data needs to be shared outside the institution it was collected in. In the case of certain diseases, there might be a need to consolidate data sets across multiple institutions to use their computing resources and algorithms for analyzing and gaining better insight. The clinical data may be shared with researchers in academia and the private sector to further transform healthcare or the development of better algorithms. Similarly, data can also be shared with pharmaceutical companies for the development of new drugs. This sharing of patient data for secondary purposes across institutions not only carries the risk of a data breach but also raises the question of consent and data privacy.

Securing Data:

• Awareness the laws where the medical practice is located is important in not only avoiding penalties, but also ensuring patient data privacy.
• De-identifying data sets can go a long way to ensure data privacy as well as compliance with laws.
• Artificial intelligence is not just for treatment and research but can also improve data security systems. Data protection and compliance controls can be built into the artificial intelligence model itself.
• Artificial intelligence algorithms can be trained to combat healthcare security challenges, such as detecting malware, identifying security breaches, protect from cyberattacks, and even prevent non-compliance with rules much more efficiently than traditional software.
• When integrating security layers in healthcare AI platforms, it's important to ensure they do not hinder healthcare professionals from their regular work. The trade-off between security and accessibility must be thoughtfully balanced in favor of efficiency.

Conclusion

AI is leveraging healthcare and is likely to make more groundbreaking innovations in the future, but concerns of data security of patients do hinder its pace. However, with adequate safeguards, measures, and compliances these concerns can be mitigated, and AI-enabled healthcare continues its progression.