The Assetization of Medical Data and Medical AI: A System Designed for Inequality and Exorbitant Prices
Introduction
The transformation of pharmaceuticals into financial assets has introduced the logics of scarcity and high prices from low-to-middle income countries to high-income countries. This shift has spurred new forms of activism, raising the question: will similar patterns emerge with the assetization of medical data and medical AI? This article explores the likelihood that the assetization of medical data and medical AI will increase inequalities and perpetuate a system designed for inequality, ultimately leading to unnecessarily high prices for healthcare services and products.
The Concept of Assetization
Assetization refers to the process of transforming goods, services, or data into assets that can be traded and valued in financial markets. In the context of pharmaceuticals, this has meant embedding drug ownership and production within financial markets, resulting in rapidly rising prices and restricted access to medicines. During my keynotes I always use the example of insulin pricing. The price of insulin has indeed increased by approximately 1200% over the past few decades. For example, the price of a vial of Humalog insulin was $21 in 1996 and had risen to around $275 by 2019. This significant price hike has made insulin unaffordable for many, leading to severe consequences. Diabetes was the eighth leading cause of death in the United States in 2021 based on the 103,294 death certificates in which diabetes was listed as the underlying cause of death, many of them because they couldn't afford insulin. This is a hard pill to swallow, knowing that Sir Frederick Banting, the physician who discovered insulin sold the patent for insulin to the University of Toronto for just one dollar. This gesture was meant to ensure that the treatment would be widely accessible and affordable for patients who needed it. Banting famously said, "Insulin does not belong to me, it belongs to the world," reflecting their intention to prioritize public health over profit.
According to the paper "Countermovements from the core: the assetization of pharmaceuticals, transparency activism and the access to medicines movement," this process has triggered new activism to contest these inequalities. Similar dynamics can be anticipated with the assetization of medical data and medical AI. In fact, I foresaw this five years ago. My foresight was based on my experiences at IBM and the lessons learned from Watson Health, combined with deep analytical thinking and applying game theory.
Medical Data and AI as Financial Assets
Medical data and AI have immense potential for improving healthcare outcomes, but their transformation into financial assets poses significant risks. Medical data, including patient records, genetic information, and clinical trial results, becomes highly valuable when aggregated and analyzed using AI. However, when these data sets and AI technologies are treated as assets, they become subject to the same financialization forces that have driven up pharmaceutical prices. In conversations with researchers at academic institutions, I discovered that some researchers within these centers need to seek approval from major pharmaceutical companies to access data stored within the hospital's electronic health records (EHR). This trend is likely to increase with the implementation of the European Health Data Space (EHDS).
The Role of Open Source Licensing
An alternative to the financialization of medical data and AI is the adoption of open source licensing. Open source licensing can create a new paradigm that allows us to transform healthcare systems and make them sustainable. By making medical data and AI algorithms openly accessible, we can foster collaboration and accelerate innovation without the barriers imposed by proprietary ownership. Open Source licensing, is the reason, why OpenAI and Google didn't manage to build monopolies in the GenAI space. The advantages of such an open approach clearly outweight its risks
Open source licensing can democratize access to medical AI technologies, enabling a broader range of researchers, developers, and healthcare providers to contribute to and benefit from these tools. This collaborative approach can accelerate the development of AI-driven healthcare solutions that are affordable and tailored to the needs of diverse populations. Moreover, it can help ensure that the benefits of medical AI are distributed more equitably, reducing disparities in healthcare access and outcomes.
In contrast, the proprietary model often leads to the concentration of control and knowledge in the hands of a few large corporations, limiting the potential for widespread benefits. Open source licensing, on the other hand, can stimulate innovation by allowing anyone to build upon existing work, leading to more rapid advancements and a more inclusive healthcare system.
Creating Equality of Opportunity and Sovereignty
Open source licensing would not only support local communities with their healthcare needs but also increase their sovereignty and create local economic opportunities. By allowing local developers and healthcare providers to adapt and innovate with medical AI technologies, communities can address specific health challenges more effectively. This can lead to the growth of local tech industries, job creation, and a more resilient healthcare infrastructure. Additionally, it can empower communities to take control of their health data and use it to make informed decisions about healthcare policies and practices, fostering greater autonomy and self-reliance.
Just as we have seen with the democratization of computer code, open sourcing medical AI would create positive effects not only on Sustainable Development Goal (SDG) 3 (Good Health and Well-being) but also on SDGs 8 (Decent Work and Economic Growth), 9 (Industry, Innovation, and Infrastructure), 10 (Reduced Inequality), and 11 (Sustainable Cities and Communities).
Inequalities in Access to Medical Data and AI
The assetization of medical data and AI is likely to exacerbate existing inequalities in several ways. Firstly, the ownership and control of medical data are often concentrated in the hands of a few large corporations or academic research institutes that sell data access. These organizations can leverage their access to vast amounts of data to develop advanced AI technologies, creating a significant barrier to entry for smaller firms and public institutions. This concentration of control limits the ability of low-income and marginalized communities to benefit from advances in medical AI.
Secondly, the cost of accessing and utilizing medical AI technologies can be prohibitively high. Companies that develop AI tools for diagnostics, treatment recommendations, and personalized medicine often charge substantial fees for their use. This can lead to a two-tiered healthcare system where only those who can afford these advanced technologies receive the best care, while others are left with inferior options.
Example of the Assetization of Scientific Publishing
The assetization of scientific publishing provides a relevant parallel and is another example I use during my keynotes. As detailed in the article "Article-processing charges as a barrier for science in low-to-medium income regions," the shift to an open-access model with high article-processing charges (APCs) has created significant barriers for researchers in low-to-middle income countries. These researchers often cannot afford the fees, which can exceed USD 10,000, limiting their ability to publish and share their work. This model has led to a concentration of scientific knowledge in wealthier regions and institutions, exacerbating global inequalities in research and innovation. Similarly, the assetization of medical data and AI is likely to result in higher costs and reduced access for underfunded healthcare systems and marginalized populations.
The Impact of Financialization on Healthcare Prices
The financialization of medical data and AI can drive up healthcare costs in a manner similar to the pharmaceutical industry. As companies seek to maximize profits, they may prioritize the development of AI tools that are highly profitable rather than those that address the most pressing public health needs. Additionally, the high prices charged for AI-driven healthcare solutions can place a significant financial burden on patients and healthcare systems.
The experience of the pharmaceutical sector, as detailed in the referenced paper, shows that financialization often leads to inflated prices. The focus on shareholder value and profit extraction results in costs that far exceed the actual expenses incurred in research and development. Similar trends can be expected in the medical AI sector, where the value of data and AI algorithms can be inflated by market speculation and the pursuit of profit.
Activism and Resistance
Just as the assetization of pharmaceuticals has given rise to transparency activism and the broader access to medicines (A2M) movement, the assetization of medical data and AI is likely to provoke new forms of resistance. Activists may push for greater transparency in data ownership and pricing, advocating for policies that ensure equitable access to medical AI technologies. Private initiatives such as my own Berlin-based Hippo AI Foundation, or even state-led activism, particularly from countries with less dominant healthcare markets, could play a crucial role in challenging the current dynamics and pushing for reforms that prioritize public health over profit.
It is disheartening to see how our policymakers prioritize short-term gains and remain entrenched in outdated narratives, claiming that open source would stifle innovation. By supporting the assetization of medical data and AI, policymakers are essentially mortgaging the future health of our children. This is completely false and based on misinformation.
Conclusion
The assetization of medical data and medical AI has the potential to increase inequalities and create a healthcare system characterized by high prices and restricted access to advanced technologies.
By transforming these essential resources into financial assets, we risk perpetuating a system designed for inequality. It is imperative to recognize these risks and advocate for policies that ensure equitable access to medical AI and data, safeguarding the potential benefits of these technologies for all, rather than a privileged few.
Embracing open source licensing can provide a viable path towards a more equitable and sustainable healthcare system, fostering innovation and collaboration while ensuring that the advancements in medical AI benefit everyone. Open source initiatives would create equality of opportunity, support local communities with their healthcare needs, increase sovereignty, and generate local economic opportunities.
Support Hippo AI is crucial to ensure equitable access to medical data and AI technologies, fostering innovation and collaboration without the barriers imposed by proprietary ownership. Please consider contributing today to help us create a more inclusive healthcare system.