The healthcare industry is increasingly adopting AI, thanks to new discoveries that are poised to redefine how patients receive care. A major part of this change is Artificial Intelligence (AI), a topic that’s talked about more and more. While there’s a lot of excitement around adopting AI, it’s natural for healthcare providers to have some concerns. 

Questions often come up about patient data, privacy, and rules and regulations. People might wonder: Will sensitive information get out? Will AI and Large Language Models (LLMs) use a patient’s private health information (PHI) for training, and then make that data public or available to others? Will the AI make a wrong choice and then tell a patient, possibly causing harm?

This fear often comes from thinking of AI as a “rogue AI” – a technology so new and developing so fast that there aren’t many widely accepted rules or best practices in place yet. 

But that is not the case. AI and LLMs can be powerful tools meant to help human abilities rather than replace them.. In fact, the percentage of healthcare providers adopting AI increased from 38% in 2023 to 66% in 2024.

Addressing a Major Concern: LLMs and HIPAA Rules

One of the biggest worries for healthcare providers when adopting AI involves patient data and privacy, especially regarding the Health Insurance Portability and Accountability Act (HIPAA). The question rightly comes up: how can LLMs, which rely on huge amounts of data, possibly follow HIPAA rules?

The answer is found in careful design and strict application. Health technology companies understand the importance of data privacy in healthcare, and they build their AI solutions with compliance at their very core. This isn’t just something that happens by accident; it’s a basic principle.

Here’s how LLMs can follow HIPAA rules:

Secure Environments: Patient health information (PHI) is processed in secure, isolated, and regularly checked environments. These environments are strengthened with strong security measures, including advanced encryption during transit and at rest.

De-identification and Anonymization: A crucial method involves removing or hiding personal identifiers from PHI before the AI processes it. This means taking out or masking all direct identifiers (like names and birth dates).

Data Governance and Access Controls: Strict rules control who can access data and how it’s used. This includes detailed access controls, making sure that only authorized staff with a legitimate reason can interact with the data, even within the AI system.

Business Associate Agreements (BAAs): Under HIPAA, any outside company handling PHI for a healthcare provider must sign a Business Associate Agreement. This legal contract requires the company to follow HIPAA’s strict security and privacy standards, holding them responsible for protecting PHI.

This shows that following these rules is a deliberate design choice, not something added later. Many companies that are designing AI tools to provide healthcare solutions are keeping these design choices at the forefront of their products.

The Essential “Human in the Loop” (HITL)

Beyond the technical safeguards, a key idea behind responsibly adopting AI in healthcare is the “Human in the Loop” (HITL) approach. Think of it as an important safety net, similar to two-factor authentication for vital decisions.

HITL means that AI systems are clearly designed to work with human oversight and involvement. The AI can offer ideas, provide suggestions, or even propose actions, but a human must always review, approve, reject, or edit before anything is done.

Why is HITL so important in healthcare?

Clinical Judgment: While AI is good at analyzing large amounts of data and finding patterns, it cannot match the detailed clinical judgment, experience, and intuitive understanding that human healthcare professionals have. AI gives valuable insights; human professionals make the final decisions based on the full patient situation, empathy, and their professional knowledge.

Ethical Oversight: Humans are vital for making sure AI is used ethically. This includes finding and fixing unfair biases in algorithms that could lead to differences in care, ensuring fairness, and being accountable for recommendations made by AI.

Error Correction: No AI system is perfect. Humans can find and correct misunderstandings, mistakes, or biases that the AI might produce. This ongoing feedback helps improve the AI’s performance over time.

Patient-Provider Relationship: AI cannot create the trust, empathy, and human connection that are fundamental to the relationship between a patient and their provider. AI tools are meant to free up professionals to spend more time on meaningful interactions with patients, not replace them.

In short, AI acts as an intelligent assistant, making things more efficient and accurate. It gives healthcare providers better information and smoother workflows, allowing them to focus on the unique complexities of each patient’s care.

Navigating Rules and Privacy Concerns

It’s important to recognize that legitimate concerns go beyond just following HIPAA rules. Worries about data breaches, unfair algorithms affecting health fairness, and who is responsible for decisions made by AI are real. The fast pace of AI development does create challenges for setting rules.

However, the rules and regulations are not standing still. Global and national organizations, like the FDA in the U.S., the EU AI Act, and Canada’s Pan-Canadian AI for Health (AI4H) Guiding Principles, are actively creating guidelines and regulations specifically for AI in healthcare. These frameworks focus on making sure AI solutions are safe, effective, transparent, and that there is accountability.

Furthermore, responsible health technology companies are building their solutions with privacy and security as core principles—an approach known as “Privacy-by-Design.” This includes:

• End-to-end encryption: Protecting data as it moves through different systems.

• Secure data storage and transmission: Setting up strong systems to prevent unauthorized access.

• Regular security checks and penetration testing: Actively finding and fixing weaknesses.

• Detailed access controls: Making sure only authorized individuals can access specific pieces of data.

Building Trust: How Companies and Regulators Are Easing Fears

Building trust in AI within healthcare requires working together among regulators, health technology innovators, and healthcare providers themselves.

Efforts are being made to make AI models easier to understand, an idea called “explainable AI.” This means providing clear information about what an AI can do, its limits, and how it arrives at its suggestions, allowing professionals to better trust and use its outputs. AI solutions go through strict testing and, when appropriate, clinical trials to confirm their safety and effectiveness. Constant monitoring and improvement after being put into use ensure that the AI works as expected in real healthcare settings.

The healthcare AI community is actively developing shared guidelines for responsible AI development and use, creating a common understanding of how to use AI ethically and effectively. Providing healthcare professionals with the knowledge and skills to use AI tools effectively and safely is crucial. This includes understanding AI’s strengths and weaknesses, and how to fit it smoothly into existing work processes.

The Final Word

The future of healthcare thrives on effective collaboration between human expertise and AI. Adopting AI in healthcare is about empowering healthcare providers, not replacing them. By actively engaging with and providing constructive feedback on emerging AI technologies, healthcare providers can play a pivotal role in shaping and unlocking exciting new possibilities for significantly improving patient care.