Artificial Intelligence (AI) advancements have jumped by leaps and bounds, seemingly overnight. Now, regulators are trying to figure out the safeguards needed to regulate this new technology with the potential to upend how we work, play and communicate. Proponents of AI argue it could radically change the health care industry and the patient-provider relationships for the better – with proper guardrails in place. Keep reading to learn more about how Capitol Hill is looking to address AI, what innovations it could bring to health care and what steps providers can take now to prepare for AI integration.

The desire to regulate is waxing; the question is, “How?”

Influential Senate leaders like Majority Leader Chuck Schumer are beginning to circle the wagons in a push to regulate AI.

• Schumer has met with experts in AI to craft legislation and is on record stating, “Our goal is to maximize the good that can come of AI. The first issue we must tackle is encouraging, not stifling, innovation. But if people don’t think innovation can be done safely, that will stifle AI’s development and even prevent us from moving forward.”
• In the past, Congress has struggled to regulate the tech industry, failing to pass legislation on data privacy and competition. AI is different; it presents new threats and opportunities.

How could AI impact health care?

• Clinical decision-making: Generative AI can analyze data from a patient’s medical records, lab results, previous treatments and medical imaging to identify potential problem areas and suggest further testing or treatment options.
• Personalized medication and care: Wearable devices can collect real-time data on a patient’s health, including heart-rate variability, blood oxygen and blood glucose levels. This data can then be fed into generative AI algorithms, which can analyze and interpret it, offering tailored recommendations and treatment options.

How should providers prepare?

It is wise for health care providers to assess whether to introduce or scale AI in health care. This might look like taking stock of the overall system’s capabilities, its level of digitization, availability and quality of data, resources and skills, then defining the system’s level of ambition for AI as it fits with their strategic goals.