In today's digital age, the question of whether to blindly trust AI with personal, financial, safety, or security matters looms large. Just like most people, the answer is likely no. Instead, we yearn to understand how AI reaches its decisions, consider its rationale, and then make our own informed choices based on that knowledge. This process, known as AI explainability, is the key to unlocking trustworthy AI – an AI that is both reliable and ethical.

Unlocking Trustworthy AI in Healthcare

As sensitive industries like healthcare continue to expand the use of AI, achieving trustworthiness and explainability in AI models becomes critical. Without explainability, researchers cannot fully validate the output of an AI model, leaving patient safety at risk. In hospitals facing staff shortages and provider burnout, the need for AI to alleviate administrative burdens and support tasks grows. But proper AI explainability must be in place to ensure patient safety.

What is AI Explainability?

As machine learning (ML) models advance, humans are tasked with understanding their decision-making processes. In healthcare, providers must retrace how an algorithm arrives at a potential diagnosis. Despite ML engines' advancements, their "black box" nature makes their calculation processes difficult to decipher. Enter explainability. AI explainability refers to the idea that an ML model's reasoning process can be explained in a way that makes sense to humans. It sheds light on how AI reaches its conclusions, fostering trust and enabling researchers and users to understand, validate, and refine AI models.In the healthcare industry, AI is making significant progress, with investments soaring to $11 billion in 2024 alone. But for systems to trust these technologies, providers need to understand their outputs. AI researchers recognize explainability as a necessary facet to address ethical and legal questions and ensure systems work as expected.

The Path to Achieving Explainability

Many researchers have turned to using AI to explain AI as a solution. This involves training a second, surrogate AI model to explain why the first AI arrived at its output. However, this method is problematic as it blindly trusts both models without questioning their reasoning.For example, an AI model may conclude a patient has leukemia, which is validated by a second AI model based on the same inputs. A provider might trust this decision at first glance, but if they had access to the AI's decision-making process, they might discover that the patient's bone marrow biopsy results were not recognized. This highlights the need for explainable AI and transparent decision-making processes.

How Explainability Serves Healthcare Professionals

Beyond diagnoses, explainability is crucial in healthcare. AI models can misinterpret data or jump to conclusions due to inherent biases. The Framingham Heart Study shows how race can be a biased input. An explainable AI model could identify this and provide more accurate risk scores.Without explainability, providers waste time trying to understand AI decisions. Explainability serves as a guide, showing the decision-making process and enabling researchers to identify and rectify errors. This leads to more accurate and equitable healthcare decisions.

What This Means for AI

While AI is being implemented in healthcare, it still has a long way to go. Incidents of AI fabricating medical conversations highlight the risks. AI should augment human provider expertise, and explainability empowers healthcare professionals to work with AI, ensuring patients receive the best care.AI explainability provides a unique challenge but also immense potential for patients. By providing transparent and understandable medical decisions, we can foster a new era of trust and confidence in healthcare.