How hot is generative AI in healthcare innovation right now? Some say you could add “AI'' to your digital health startup’s name and double your funding goal before the week is out.

While that might be a slight exaggeration, you can confidently bet that generative AI will revolutionize healthcare—and it won’t take long. But what exactly is AI, how is it impacting the healthcare industry, and what are the legal and regulatory concerns you need to understand if you are building a healthcare AI solution? You can ask ChatGPT these questions, but we recommend starting here.

In this post, we’ll first define “generative AI” and where it fits within the broad category of machine learning.

Then we’ll show some ways generative AI is making waves in healthcare right now and shed some light on the legal considerations that come with each of these changes.

You’ll come away with (1) a better understanding of generative AI’s potential in improving healthcare and (2) an understanding of the business and legal challenges that come with specific applications of this technology.

Now let’s get to it.

Understanding the Difference Between Generative AI and Machine Learning

Right now there are a lot of hot buzzwords about artificial intelligence. But what do the key terms mean?

Let’s start with machine learning (ML), the basic building block of AI (defined below). ML is a technique used to teach computers to learn from data, without explicitly programming the machines for each specific task they carry out. For example, an ML model can learn to identify malignant tumors by studying thousands of MRI scans labeled as “malignant” or “benign.” Once trained, this model can analyze new scans and determine if they show signs of malignancy.

Artificial intelligence (AI) encompasses a broader range of capabilities beyond machine learning. AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term can also be applied to any machine that exhibits traits associated with a human mind, such as learning and problem-solving. For instance, AI systems can interpret complex medical data, make decisions based on that data, and even interact with patients using natural language processing. This technology can enable the creation of intelligent diagnostic tools and decision-making systems in healthcare, further enhancing the capabilities of medical professionals and improving patient care outcomes.

Remember, these are broad concepts and there's a lot of overlap between them.

Generative AI Use Cases and Legal Considerations

You can think of Generative AI in healthcare as an advanced virtual assistant who learns from existing medical data, then utilizes that knowledge to create new products and services. With generative AI, innovators are developing sophisticated algorithms for personalized medicine, creating virtual models of patient health scenarios to improve diagnosis accuracy, and creating products that analyze extensive databases of patient health records and then "conceive" innovative digital health solutions based on that wealth of information.

Now let’s look at two exciting use cases of generative AI in healthcare, and the legal issues companies should consider when implementing these technologies in their business models.

As we explore each area, you'll find out how these AI applications can impact the company you’re building and the potential legal and ethical hurdles you might face.

Synthetic Data Generation

The more data you have, the easier it is to spot patterns, predict outcomes, and make decisions. Using synthetic data generation, companies can create realistic but anonymized datasets that mimic real-world information—then use that data to make AI models more accurate while still playing by HIPAA rules.

This synthetic data, created by generative AI, can include text, images, audio, data, tables, and other information which are already pre-labeled. This means no waiting on humans to correctly annotate every bit of data before analysis and virtually no limit to the number of data sets available.

Synthetic data can be used in cases where actual data is hard to obtain, too costly, or when privacy concerns restrict the use of real-world information.

Before the decade ends, industry research firm Gartner predicts synthetic data will outstrip actual data in training AI models.

This is a big deal for healthcare innovation companies. Synthetic data can:

• Address the scarcity of quality data due to the sensitive nature of health records, issues related to consent, and legal and ethical restrictions.

• Improve research and development–synthetic data can be used to simulate different situations, patient demographics, or conditions that might be expensive, unethical, or impractical to replicate with real-world trials.

• Facilitate better collaboration and data sharing between institutions or companies, without raising privacy concerns.

The applications of generative AI for predictive analytics and healthcare research are incredibly promising but innovators in the space must navigate a number of legal considerations when generating this type of data for healthcare. Below, I’ve described two common applications and a sampling of the types of legal issues that arise in each.

Privacy Regulations Compliance

Because some real data needs to inform the parameters of the synthetic data set, patient privacy regulations still apply. This is especially important for unusual health conditions which may make re-identification easy. Synthetic data might inadvertently reveal sensitive information about the individuals in the original dataset. You should employ robust data anonymization techniques and conduct thorough validation to ensure that synthetic data does not retain identifiable information.

You’ll also want to consider where the data is stored and processed, taking into account geographical differences in privacy regulations. If the data is moving from one country to another, you’ll need to make sure you are compliant in the sending and receiving locations.

Intellectual Property Rights Protection

Companies using generative AI tools to create synthetic data also need to keep an eye on their intellectual property. You may be able to patent or copyright the proprietary algorithms you're using for synthetic data generation. (Hear more on IP tips for digital health companies in this episode of Decoding Healthcare Innovation.)

Bias and Inaccuracy

Because generative AI models inherit the biases present in the data upon which they’re trained, if the original data set is skewed towards certain demographics, behaviors, or patterns, the synthetic data generated will likely reflect these biases. This can result in synthetic data sets that are not representative of the real world–in fact, synthetic data sets can amplify biases or even introduce new ones.

Bias and inaccuracy in synthetic data sets used to build your product presents both legal and ethical challenges. In particular, we’ve seen an uptick in legal actions related to violations of state and federal anti-discrimination laws. You should regularly review and update AI algorithms to reduce algorithmic bias and employ techniques to ensure the synthetic data is representative of the broader population or phenomenon you are targeting.

Personalized Treatment Plans

Generative AI can process vast amounts of data, spotting patterns and trends that would be difficult for humans to see. Practitioners can use these AI models to analyze patient data and generate personalized treatment plans.

Instead of a one-size-fits-all approach, personalized treatment plans can take into account the individual patient's unique factors, like their genes, environment, lifestyle, and even their specific type and stage of disease.

With generative AI models, healthcare providers can change treatment plans on the fly, making sure they're giving the best and most up-to-date care. Because AI can juggle different types of data, it gives a complete picture of a patient's health, paving the way for a treatment plan that's tailored just for them. It may also predict health issues before they happen, letting doctors step in early—possibly even before symptoms start showing up.

As this technology becomes more accessible, it could help increase health equity by offering high-quality personalized healthcare advice to those with limited access to providers.

The advancements in AI-enabled personalized treatment plans sound promising, but they also come with challenges.

Patient Care Standards and Informed Consent Requirements

Incorporating generative AI into personalized treatment plans requires a thorough understanding of relevant patient care standards, like the ones established by the American Medical Association (AMA).

If your company uses generative AI to create custom treatment plans, make sure your use of the technology adheres to these guidelines, created to ensure patients receive high-quality care. The guidelines include things like:

• Evidence-based practice: Healthcare professionals should rely on evidence-based practices when using generative AI tools, ensuring that any recommendations are supported by scientific research and clinical trials.

• Data privacy: Generative AI systems often require access to sensitive patient data, so you must comply with privacy regulations like HIPAA when handling this information.

• Informed consent: Patients have the right to be fully informed about their treatment options, including potential risks and benefits associated with using generative AI technology. Providers should obtain informed consent before incorporating this technology into a patient's care plan.

In addition, AI solutions that operate without the close supervision and review of licensed providers must carefully consider state scope of practice and licensure rules. If your AI is “practicing medicine”, you’re likely not operating in compliance with state patient protection laws.

Liability Risk Management

Liability Risk Management is an essential consideration for healthcare innovation companies that want to use generative AI to create treatment plans. 

As AI begins to play a larger role in decision-making in healthcare, questions around liability become more complex. Who is held accountable if an AI system recommends a treatment plan that leads to adverse patient outcomes—the healthcare provider, the AI developer, or the company deploying the AI? This uncertainty can lead to liability risks. 

Healthcare innovation companies need to understand these issues and develop risk management strategies. This can involve:

• Establishing clear responsibility and accountability guidelines

• Ensuring proper AI system validation and testing

• Adopting transparent documentation practices for AI decisions

• Regularly auditing AI systems to detect any potential issues as early as possible 

Navigating Patent Eligibility Challenges

Navigating patent eligibility for AI-generated solutions can be challenging due to the unique characteristics of AI.

AI-generated designs might not be seen as “human invention,” which is typically required for a patent. Explaining how generative AI comes up with particular designs can be difficult due to the “black box” nature of many of the models. This can make it hard to demonstrate that your solution fulfills the "non-obvious" requirement of patent law.

Resolving Ownership Disputes

There are also potential issues around ownership. When AI is involved, it can blur the traditional lines of who (or what) is the actual inventor. Generative AI systems that are trained on large datasets may also unknowingly infringe on existing patents.

Here are some critical considerations to help steer you clear of ownership disputes:

• Data rights: If you’re using proprietary datasets as input for generative AI tools, you need to establish clear agreements regarding data rights, including any limitations on use or sharing within collaborative projects.

• Licensing agreements: When partnering with other organizations or licensing third-party technology, create well-defined terms outlining each party's rights and responsibilities concerning intellectual property generated through collaboration.

• Inventorship determination: As generative AI systems become more autonomous in creating novel designs, determining inventorship can be challenging.

FDA Considerations

Products and services using generative AI to assist in the diagnosis and treatment of patients may result in significant regulatory exposure. Depending on the level of autonomy and risk inherent in the business model, an AI company might find themselves categorized as a medical device, and therefore subject to registration or approval by the FDA, which can be a lengthy and expensive process.

The FDA's focus is on ensuring that such technologies are safe, effective, and reliable, particularly in high-risk scenarios where inaccurate diagnoses or treatment recommendations could have severe consequences. In some cases, you can modify your business model to avoid FDA scrutiny. This is where regulatory counsel can be worth their weight in gold.

Addressing Ethical Considerations

Beyond legal requirements, there are several ethical considerations related to the use of generative AI in the context of healthcare services. Some key issues include:

• Reducing data bias: Ensuring fairness in machine learning algorithms requires careful attention to training data quality. Developers should strive for diverse datasets that represent various demographics and avoid perpetuating biases within clinical practice.

• Auditing algorithms: Regularly auditing algorithms used within digital health applications can identify unintended consequences or inaccuracies stemming from model limitations. Being transparent about how algorithmic decisions are made can build a bridge of trust among providers, patients, and those who develop the AI systems.

• Implementing human oversight: While generative AI can offer valuable insights for healthcare professionals, there must be a balance between technology and human expertise. Clinicians should remain actively involved in interpreting AI-generated content to ensure accurate diagnoses and appropriate treatment recommendations.

Legal Guidance for Generative AI Integration in Healthcare Innovation

As we've seen, the healthcare industry stands to gain tremendously from the integration of generative AI in its many facets—including medical diagnostics, personalized treatment plans, bioengineering, and behavioral health. These advancements promise to make care more precise, personal, and effective.

But healthcare innovation companies must be vigilant in understanding and adhering to the legal and ethical considerations accompanying such technological adoption—especially when operating in a complex regulatory zone that struggles to keep pace with innovation.

At Nixon Gwilt Law, we’ve been working with AI innovators like you long before the public ever heard of ChatGPT, and we can help you safely innovate—whether you are operating at the leading edge or considering the services of a company that does.

Click here to find out how we can help you use generative AI to fuel your business.

Related Resources:

• Podcast: Practical Applications of AI in Healthcare with Joseph Zabinski, PhD of OM1

• Podcast: AI’s Role for Clinicians and Patients in Behavioral Health with LifeStance Health Chief Digital Officer Pablo Pantaleoni

• Blog: The Enterprise Potential of ChatGPT for Digital Health