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Challenges And Best Practices for Securing AI In Healthcare

Artificial Intelligence (AI) has emerged as a transformative force in healthcare, revolutionizing patient care, diagnosis, treatment, and operational efficiency. It has brought automation to help healthcare processes become much more manageable. However, the integration of AI in healthcare comes with significant challenges in ensuring patient safety, which could emerge from errors in AI Algorithms, data security, patient privacy, and ethical use. Implementing robust security measures that call for collaboration between agencies such as the FDA (Food and Drug Administration) while harnessing the potential of AI in healthcare is crucial for safeguarding sensitive information and maintaining trust in the healthcare system.

What are the benefits of AI in Healthcare

Enhanced performance

The introduction of AI into the healthcare sector is not yet solid because of several considerations before an AI tool can be considered “safe to use.” Yet, it has proven useful in pushing human performance boundaries and ensuring more accuracy and efficiency in some healthcare procedures. It is worth noting that AI tools are already capable of predicting injuries before their occurrence, which allows medical practitioners to take precautions. An example will be a program developed by Google Health to predict acute kidney injury two days before its occurrence.

Automation

AI has done extremely well in the areas of automation in most industries. The healthcare industry allows practitioners to make better use of their time by automating medical tasks like record keeping, screen reading, and managing data. AI models have found even more relevant use because of their capability to distinguish and prioritize the availability of patients’ relevant information through effective data analysis and structuring.

Resource Management

Healthcare centres sometimes fall short of necessary supplies due to oversight, but AI can bring a change by ensuring prompt notification of the necessary authority when required.

Challenges associated with AI in Healthcare

Data Availability

We can’t overemphasize the reliance of AI on the availability of credible data, but this is quite a challenge in the healthcare sector. When it comes to patient data, fragmentation from multiple healthcare and insurance organizations makes patient data exist in bits in different systems, causing errors and decreasing data availability.

When it comes to the automation of treatment procedures, there is always research leading to the discovery of a better way to handle sicknesses, and it implies the need for a regular update in the AI database to prevent it from using old treatment methods when there are new and better ones.

Privacy Concern

Training an AI model requires an incredible amount of data. Patients’ data must remain confidential to them and their healthcare professionals. AI in healthcare systems also needs this information to offer the best services, which invariably means committing patient’s confidential health information to AI. This question might arise, “How secure is this AI platform?

Regulatory bodies like PHI (Protected Health Information) and HIPAA (Health Insurance Portability and Accountability) exist to regulate activities in the healthcare sector. Still, they have warned about indiscriminately committing data to a third party, which could be an AI vendor. Another consideration is the ever-changing cyber threat landscape, which is consistently changing to beat the existing regulations. Confidentiality is critical to consider regarding AI in healthcare. Considering the high level of sensitivity of patient data, it is important to enforce strict access control measures to reduce the risk of breaching confidentiality.

Possibility of Injuries and Error

Unlike most organizations, any error could be severe in the healthcare sector. Since AI systems are not immune to cyberattacks, they are susceptible to all levels of cyber attacks. With this in mind, monitoring AI use in the healthcare sector is important. It is best practice to ensure regular updates, not fully relying on AI systems when implementing AI solutions in healthcare.

Reduction in Professionalism

Overreliance on AI systems in healthcare could be hazardous, as mentioned above. This might be evident in how specialists behave due to task automation or the potential for error when Professionals get too comfortable and stop researching beyond AI systems’ recommendations.

Best Practices for Securing AI in Healthcare

Robust Data Encryption and Access Controls

Healthcare data is strictly confidential because it contains sensitive information, and enforcing strict access control is necessary. Only some authorized persons should have access to healthcare records, and one of several best practices would be to log who accessed the data and when the data was accessed at all times. Data encryption is another best practice for data at rest and in transit, adding another security level for healthcare data.

Regular Security Assessments and Updates

Conducting frequent security assessments and updates to AI systems is essential. Regular patches, software updates, and vulnerability assessments help mitigate potential risks and ensure systems are resilient against evolving cyber threats.

Employee Training and User Awareness

Training healthcare professionals on cybersecurity best practices and user awareness about potential cyber threats can significantly reduce human-related errors. Educating employees on identifying phishing attempts and adhering to security protocols can also strengthen an organization’s overall security posture.

Collaboration and Information Sharing

Facilitating collaboration between healthcare organizations, regulatory bodies, and cybersecurity experts fosters information sharing on emerging threats and best practices. This collaboration helps create a unified approach towards addressing cybersecurity challenges in healthcare.

Conclusion

Securing AI in healthcare is an ongoing endeavour that requires a multi-faceted approach. As technology advances, integrating innovative security measures, enhancing regulatory frameworks, and fostering a culture of security awareness will be pivotal in fortifying the use of AI in healthcare. Embracing these best practices and overcoming challenges will ensure that AI continues to drive positive transformations in healthcare while safeguarding privacy and data security.

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