Skip to content Skip to footer

Will AI Really Come with Nutrition Labels? The Rising Need for Transparency and  Accountability in AI for Healthcare 

Summary: Artificial intelligence (AI) in healthcare may soon come with “nutrition labels,” akin to those on food and drugs, to provide transparency on how AI solutions are trained and their data sources, addressing concerns such as data privacy, bias, and explainability, while offering benefits like aiding diagnostics, streamlining administrative tasks, and enabling personalized medicine.

Going way back to the Pure Food & Drug Act of 1906, truth in labeling was enacted to protect consumers from adulterated and unsanitary food and drugs. Today, when artificial intelligence (AI) is permeating all aspects of healthcare, the premise of nutrition labels is once again taking center stage – requiring AI developers to reveal “the ingredients” contained in the AI solutions they create.

This is a topic I recently explored in an article for Physician’s Practice. As we move full steam ahead into the AI generation, the healthcare industry is taking a cue from the food and drug sectors, placing nutrition labels on AI-driven solutions, to share how they were trained and on what data. The goal is to provide transparency to doctors, clinicians and other health professionals so they can make informed decisions about how AI supports their treatment decisions for patients.

While nutrition labels are not currently a regulatory requirement, Federal regulators are working to enact legislation. Spearheaded by the Office of the National Coordinator for Health Information Technology (ONC), the proposed nutrition label requirements would require disclosing how the app was trained, how it performs, how it should be used (or not used). This is the same group also responsible for certifying electronic health record (EHR) software.

There are clear lines being drawn in the sand. While many applaud the transparency and accountability that nutrition labels offer, others are pushing back on nutrition labels, proposing that the rule could compromise proprietary information and hurt competition.

As I shared with Physician’s Practice readers, despite the naysayers, it’s vital that we ensure its safe and transparent use. Consider the following AI risk factors:

Data Privacy and Security: By its data-driven nature, AI handles sensitive patient data, which can raise concerns about privacy and security. Unauthorized access to health data could lead to breaches and misuse.

Bias and Fairness: If the datasets used to train AI algorithms are biased, the AI systems may produce biased results, leading to disparities in healthcare outcomes across different demographics.

Explainability: For every decision made by an AI tool, there must be a way to explain how the algorithm arrived at the decision. Given the complexity of AI and the massive datasets required to make them effective, it can be difficult to explain the decision-making process today.

Regardless of the outcome of nutrition label laws, AI is being increasingly utilized by clinicians and medical professionals in various aspects of healthcare. Consider some of the following applications:

Diagnostic Imaging: AI is helping to analyze medical images such as X-rays, MRIs, and CT scans, to aid in the early detection of diseases and abnormalities.

Administrative Tasks: AI streamlines administrative tasks, such as appointment scheduling, billing and claims processing, reducing administrative burden and improving efficiency.

Clinician Notes: According to the National Institutes of Health (NIH), doctors spend around 35 percent of their time documenting patient data. AI assistants are playing greater roles helping them generate progress notes from a patient-provider conversation in the exam room.

Personalized medicine: AI is helping doctors analyze patient data, including genetic information, to tailor treatment plans based on individual characteristics, leading to more effective and targeted interventions.

Despite the immense benefits of AI in healthcare, there are some very real risks, and clinicians should have the opportunity to understand what they’re getting into. Nutrition labels can provide that transparency, so that they can make more informed decisions that affect patient health.

Get the best blog stories in your inbox!