Trust is all you need

Privacy and data security: Safeguarding the vaults of trust

The inviolability of patient privacy necessitates tighter governance and ownership of data used by GenAI. It needs meticulous anonymization of unstructured datasets and control of LLMs referencing multivariate data points to prevent personally identifiable information (PII) leaks. Crucial in this regard is the understanding that while algorithms can offer insights and advice, the ultimate decision-making must remain in the hands of clinicians and Healthcare professionals.

GenAI's voracious appetite for data is undeniable, but it can’t come with the risk of unauthorized access and data breaches. Without careful cleaning, curating and auditing of the training data selected, there is a risk that GenAI could unwittingly expose patient data to outsiders. Robust cybersecurity measures, stringent access controls, encryption protocols and defense mechanisms against adversary attacks on LLMs or GenAI-based systems can provide an added layer of protection.

Bias and fairness: Ensuring equitable healthcare

GenAI-driven personalized medicine could create hundreds of billions in annual value by improving health outcomes and treatment efficacy. But the question that surfaces is this: How does one ensure the equitable and ethical distribution of such value?

GenAI picks up the inherent bias in human data that manifests as disparities in Healthcare outcomes and access to services. We've seen this human bias before in the way women and ethnic minorities receive different medical treatments. Implicit biases are hard to detect and particularly insidious, as they perpetuate false knowledge under the guise of factual data. The need, therefore, is to adopt fairness-aware algorithms, re-train AI systems, and integrate GenAI with advanced mechanisms to proactively detect and mitigate these biases. It underscores the urgency to build frameworks that are inherently equitable and devoid of biases.

Accountability, transparency and liability: The pillars of responsibility

GenAI applications are fraught with questions of accountability and liability. Who is responsible for GenAI-driven decisions? What happens when it starts hallucinating, churning out misleading information, or making factual errors? How do we detect and control the unwanted creativity of GenAI?

Accountability in the context of GenAI in Healthcare is multi-faceted and involves several stakeholders—from developers to players (such as payers, providers, and Medtech and life sciences companies) to end users.

• Developers must make sure AI models are transparent, unbiased and super secure by building the right controls.
• Healthcare players should know that the buck ultimately stops with them for all decisions they make, even with AI recommendations.
• End users must have transparency to make informed choices when it comes to their data and care decisions.

Driving transparency requires interpretability, explainability, justified opacity and auditability. Responsible AI ensures AI technologies are developed and used in a manner that is ethical, transparent and accountable. And responsible AI frameworks need to be applied consistently to GenAI in the context of Healthcare solutions.

When it comes to liability, the legal ramifications of AI-induced errors in Healthcare are intricate. Determining liability is a complex dance—is it the AI model that malfunctioned, or did the Healthcare provider erroneously interpretate the results? Clearing up this confusion demands solid regulatory frameworks to define liability boundaries while still leaving room for innovation.

Another essential consideration: are we risking a “GenAI divide” in Healthcare mirroring the digital divide, where unequal access could exacerbate disparities in outcomes that challenge our commitment to equitable Healthcare for all?

Striking the balance: Healthcare quality and trust framework

Crafting an ethical framework isn’t about putting the brakes on innovation but rather about fostering responsibility. It requires a collective dedication to transparency and fairness and the continual reassessment of ethical guidelines to ensure we build trusted solutions that maximize value from GenAI.

While regulations around AI are evolving, Healthcare players cannot wait around to tick the compliance boxes. They need to be proactive in syncing with the evolving ethical scene to make sure GenAI rolls out in Healthcare in a fair, responsible and patient-focused way.

There is a need to build platform-agnostic quality and trust frameworks to measure, validate and monitor GenAI solutions to ensure quality, trustworthiness and consistency of outcomes. Such frameworks should fully integrate into existing quality management systems while synthesizing, actionizing and staying updated on regulatory recommendations. However, lack of clarity on data governance from a privacy, compliance and security point of view, and perceived ease of use of GenAI tools, can create a false sense of feasibility for those building solutions.

With a "human-in-the-loop" approach, responsible AI practices and equitable access to GenAI technologies, I am optimistic about creating a future where GenAI serves as a tool for equitable Healthcare for all. To extend the Hippocratic Oath's tenet of "primum non nocere," the first step is to do no harm. A glorious consequence of that is doing good.