About Black-Box Medicine

Healthcare in the United States is a complex and controversial subject. Approximately 30 million Americans are uninsured and at risk of financial ruin if they become ill or injured. Advanced science and technology could ease some of the challenges around access, diagnosis, and treatment if legal and policy frameworks allow innovation to balance patient protection and medical innovation.  

Artificial intelligence (AI) is rapidly moving to change the healthcare system. Driven by the juxtaposition of big data and powerful machine learning techniques, innovators have begun to develop tools to improve the process of clinical care, to advance medical research, and to improve efficiency. These tools rely on algorithms, programs created from health-care data that can make predictions or recommendations. However, the algorithms themselves are often too complex for their reasoning to be understood or even stated explicitly. Such algorithms may be best described as “black-box.” This article briefly describes the concept of AI in medicine, including several possible applications, then considers its legal implications in four areas of law: regulation, tort, intellectual property, and privacy.

Make sure to read the full article titled Artificial Intelligence in Health Care: Applications and Legal Issues by William Nicholson Price II, JD/PhD at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3078704

When you describe your well-being to ChatGPT it takes about 10 seconds for the machine to present a list of possible conditions you could suffer from, common causes, and treatment. It offers unprecedented access to medical knowledge. When you ask how ChatGPT concluded your assessment, the machine is less responsive. In fact, the algorithms that power ChatGPT and other AI assistants derive their knowledge from a complex neural network of billions of data points. Information – some publicly available, others licensed and specifically trained into the model’s predictive capabilities – that is rarely analyzed for accuracy and applicability to the individual circumstances of each user at the time of their request. OpenAI, the current market leader for this type of technology, is using reinforced learning from human feedback and proximal policy optimization to achieve a level of accuracy that has the potential to upend modern medicine by making healthcare assessments available to those who cannot afford it. 

Interestingly, the assessment is something of a black box for both medical professionals and patients. Transparency efforts and insights into the algorithmic structure of machine learning models that power these chat interfaces still seem to be insufficient to explain reason and understanding about how the specific recommendation came to be and whether the prediction is tailored to the users’ medical needs or derived from statistical predictions. The author paints a vivid picture by breaking down the current state of medicine/healthcare and artificial intelligence and characterizing it with the “three V’s”: 

  1. Volume: large quantities of data – both public and personal, identifiable (health) information that is used to train ever-voracious large language models. Never before in history has mankind collected more health-related data through personal fitness trackers, doctor appointments, and treatment plans than it does today.  
  2. Variety: heterogeneity of data and access beyond identity, borders, languages, or culture references. Our health data comes from a wealth of different sources. While wearables track our specific wellbeing; location and travel data may indicate our actual wellbeing. 
  3. Velocity: fast access to data – in some instances with seconds to process medical data that otherwise would have taken weeks to process. Arguably, we have come a long way since WebMD broke down the velocity barrier. 

The “three V’s” allow for quick results, but usually lack the why and how a conclusion has been reached. The author coined this as “Black-Box Medicine”. While this creates some uncertainty, it also creates many opportunities for ancillary medical functions, e.g. prognostics, diagnostics, image analysis, and treatment recommendations. Furthermore, it creates interesting legal questions: how does society ensure black-box medicine is safe and effective and how can it protect patients and patient privacy throughout the process? 

Alomst immediately the question of oversight comes to mind. The Food and Drug Administration (FDA) does not regulate “the practice of medicine” but could be tasked to oversee the deployment of medical devices. Is an algorithm that is trained with patient and healthcare data a medical device? Perhaps the U.S. Department of Health and Human Services or local State Medical Boards can claim oversight, but the author argues disputes will certainly arise over this point. Assuming the FDA would oversee algorithms and subject them to traditional methods of testing medical devices, it would likely subject algorithms to clinical trials that couldn’t produce scientific results because an artificial intelligence, by virtue of its existence, changes over time and adapts to new patient circumstances. Hence the author sees innovation at risk of slowing down if the healthcare industry is not quick to adopt “sandbox environments” that allow safe testing of the technology without compromising progress. 

Another interesting question is who is responsible when things go wrong? Medical malpractice commonly traces back to the doctor/medical professional in charge of the treatment. If medical assessment is reduced to a user and a keyboard will the software engineer who manages the codebase be held liable for ill-conceived advice? Perhaps the company that employs the engineer(s)? Or the owner of the model and training data? If a doctor is leveraging artificial intelligence for image analysis – does it impose a stricter duty of care on the doctor? The author doesn’t provide a conclusive answer and courts yet have to decide case law of this emerging topic in healthcare. 

While this article was first published in 2017, I find it to be accurate and relevant today as it raises intriguing questions about governance, liability, privacy, and intellectual property rights concerning healthcare in the context of artificial intelligence and medical devices in particular. The author leaves it to the reader to answer the question: “Does entity-centered privacy regulation make sense in a world where giant data agglomerations are necessary and useful?”   

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