Rushing AI Into Healthcare Risks Repeating Telemedicine’s Costly Security Mistakes

The Risks of Rapid AI Adoption in Healthcare

During the COVID-19 pandemic, telemedicine rolled out quickly, keeping healthcare accessible for many. However, in the rush to implement remote care, some hospitals overlooked critical cybersecurity checks. This led to vulnerabilities such as unvetted apps, weak encryption, and unsecured endpoints—opening doors to cyberattacks and exposing patient data.

Now, as healthcare organizations adopt AI at breakneck speed, it's worth asking: Are we repeating the same mistakes?

AI in Healthcare: Promise Meets Risk

AI is transforming medicine through new diagnostic tools, imaging, clinical decision support, and workflow automation. Many hospitals are eager to implement AI solutions to address workforce shortages, reduce burnout, and improve efficiency. But rapid deployment often outpaces the ability to secure these technologies properly.

Similar to telemedicine's quick adoption during a crisis, AI is being fast-tracked—sometimes implemented in months or even 90 days. Early telemedicine relied heavily on consumer-grade platforms, while today's AI solutions often come from startups. Both scenarios involve high reliance on third-party tools and shadow IT, with security oversight frequently bypassed.

Regulatory Gaps and Security Challenges

During the telemedicine surge, HIPAA regulations were temporarily relaxed. AI regulation is still catching up, leaving hospitals to adopt AI without clear guidelines or risk assessments. Many AI platforms get integrated without involvement from Chief Information Security Officers (CISOs) or formal governance, resulting in unmonitored data flows and inconsistent access controls.

While bodies like the FDA and NIST work on AI-specific guidance, hospitals often lack comprehensive audit trails or AI-specific risk frameworks.

Data Security Concerns with AI

Telemedicine introduced new risks around data transmission. AI adds complexity by handling massive volumes of sensitive clinical data used for training and real-time decisions. This data must be secured, anonymized, and monitored throughout its lifecycle to avoid breaches or misuse.

The lessons from telemedicine's security gaps offer a clear warning: rushing AI adoption without proper safeguards puts patient safety and privacy at risk.

Best Practices for Safe AI Implementation

• Integrate security reviews directly into AI design and procurement processes.
• Develop AI-specific data governance and patient consent frameworks.
• Perform thorough risk assessments of third-party AI vendors.
• Train clinical staff on AI limitations, risks, and proper usage.
• Maintain detailed logs of AI interactions and outputs for auditing purposes.

Balancing Innovation and Safety

AI in healthcare holds great promise to reduce administrative tasks, improve diagnostic accuracy, and streamline workflows. However, unlike telemedicine—which mainly provided communication channels—AI can directly influence clinical decisions. If AI models are manipulated, poorly trained, or exposed to unvetted data, the consequences could include misdiagnoses or inappropriate treatments.

Failing to secure AI properly risks eroding patient trust and diminishing the benefits these technologies aim to deliver.

Healthcare leaders must embed cybersecurity at every stage of AI adoption—from selecting vendors to ongoing model monitoring. Innovation should never come at the expense of patient safety and data security.