Many believe that AI will shape the future of healthcare, with advancements in machine learning and data analytics already transforming diagnostics, treatment planning, and patient care. From AI-powered imaging that enhances early disease detection, to predictive algorithms that personalize treatments, there is no doubt about the immense benefits and potential AI brings to healthcare.
This month’s launch of Microsoft’s Dragon Copilot marks a major milestone in that direction. Designed to streamline clinical workflows, Dragon Copilot leverages real-time ambient listening, automated documentation, and intelligent task management to reduce administrative burdens, allowing healthcare professionals to focus more on patient care.
This is a significant step toward revolutionizing healthcare. However, to fully unlock AI’s potential, we must look beyond individual hospital workflows and address a critical missing piece: secure, privacy-preserving data sharing across the entire healthcare ecosystem. Without it, even the most advanced AI solutions will remain constrained by data silos, limiting their ability to drive truly transformative change.
Read more about Roseman Labs' collaboration with Motius, uniting Retrieval Augmented Generation with encrypted computing.
The barriers to AI-powered Healthcare
Amid geopolitical uncertainty and increasing cyber threats, healthcare organizations find themselves at a crossroads. Despite the promise of AI in healthcare, unlocking its full potential is hindered by three major challenges.
1. Cybersecurity risks
Healthcare is one of the most targeted industries for cyberattacks, with a sharp increase in ransomware incidents across Europe in 2024. The INC Ransom gang, which emerged in 2023, has increasingly targeted healthcare organizations, breaching sensitive medical data, including blood test results for HIV and cancer. In February 2024 alone, over 50 million records were exposed across Europe due to cyberattacks [IT Governance Europe, 2024].
Ransomware gangs infiltrate hospital systems, deploy malware, and exfiltrate patient data. They cripple operations and demand payment in Bitcoin, threatening to leak stolen information if demands aren’t met [The Guardian, 2024].
This escalating threat presents a paradox. AI solutions can help revolutionize patient care as more data-driven insights are made. However, hospitals are increasingly reluctant to share patient data due to increased security risks.
2. Privacy & trust concerns
Patients and healthcare providers worry that shared medical data could be misused or fall into the wrong hands.
With high-profile breaches making headlines, patients are more skeptical than ever about where their medical data goes and who has access to it. If they fear that their information could be sold, used for unauthorized research, or even exposed in cyberattacks, they may withhold crucial details from their providers, impacting care quality.
Hospitals and clinics are subject to strict regulations like GDPR and HIPAA. Any mishandling of patient data can lead to legal consequences, financial penalties, and reputational damage. As a result, many institutions are reluctant to share data, even when it could advance AI-driven medical research and improve patient outcomes.
If healthcare professionals and patients do not trust that AI solutions will keep data secure and uphold ethical standards, they will resist adoption. This hesitation can delay innovation, limiting the ability of AI tools to deliver real-world impact in diagnosis, treatment recommendations, and operational efficiency.
3. Regulatory complexity
While GDPR and other privacy laws enforce strong data protection standards, inconsistent interpretations across borders create roadblocks for cross-institutional, seamless data exchange. This leads to legal uncertainty for healthcare providers and research institutions operating across multiple jurisdictions.
Moreover, many healthcare institutions struggle to share patient data internationally, even when it could improve treatments and accelerate medical research. The challenge lies in navigating local compliance requirements, which often require complex legal agreements or additional security measures.
These regulatory inconsistencies hinder AI’s potential in healthcare. AI models require large, diverse datasets to improve accuracy and effectiveness, but when institutions are unable to legally or logistically share data, AI development stagnates. Bridging regulatory gaps is crucial for unlocking AI’s full potential while maintaining compliance.
Bridging the gap: Encrypted AI
To maximize the benefits of AI powered solutions like Microsoft’s Dragon Copilot, we need a way to collaborate across institutions, without exposing patient records. This is where solutions like Roseman Labs’ encrypted computing platform step in.
Roseman Labs delivers the world’s fastest, most secure and easy-to-use encrypted computing platform, designed to unlock AI’s full potential while securing the world's most sensitive data.
We empower organizations to collaborate on data securely, unlocking data-driven insights while staying fully compliant, unlike traditional approaches that force organizations to choose between security, compliance, and innovation.
Our impact is already evident in real-world applications, such as our collaboration with UMC Utrecht, part of a network of Dutch University hospitals. As highlighted in this case study, Roseman Labs enables secure, privacy-preserving analysis of sensitive patient data that would otherwise be too risky to share. By leveraging our platform, UMC Utrecht extracts valuable insights without ever exposing patient data.
This example underscores a critical shift in AI-driven healthcare: the ability to securely share and analyze data across domains, including hospitals, research institutions, and public health agencies, without violating privacy regulations.
With Roseman Labs, AI models and analytics tools can process encrypted data without ever decrypting it, ensuring both security and compliance. This enables:
- Hospitals to collaborate across institutions and borders without violating privacy regulations or exposing proprietary data.
- The entire healthcare ecosystem to access real-world data insights while keeping patient records confidential.
- AI-powered systems to operate with full transparency and compliance, ensuring data remains protected at all times.
By removing privacy and security barriers, Roseman Labs unlocks the true potential of AI-driven healthcare, enabling better patient outcomes, faster innovation, and a more connected, data-driven future.
AI in Healthcare must be secure to be transformative
The future of AI in healthcare isn’t just about efficiency, it’s about creating an interconnected, privacy-first ecosystem where insights can be shared securely to drive better patient outcomes. AI alone isn’t enough.
True transformation happens when AI and privacy-preserving technologies work together, enabling secure, data-driven innovation without compromising trust.
Generate new insights on sensitive data with Roseman Labs’ encrypted computing platform. Want to find out how your organization can do that? Contact us using the form below.
