Explainability in healthcare is a contentious topic with long-term implications. Health services management, clinical decision-makers, predictive medicine, patient data, and diagnostics increasingly use AI. However, despite achieving high performance, AI is still regarded as a black box.
One reason for this is the lack of trust, which, in turn, emerges from a lack of transparency. For instance, the healthcare practitioner should be able to explain why a particular course of treatment is better. If the AI system does not provide that ‘explainability,’ it can affect AI adoption.
So, explainable AI (XAI) in healthcare are technologies that enable practitioners to understand “why” the system made certain decisions. In other words, it explains the AI system’s inherent logic.
Implications of explainable AI in healthcare
Many high-performing black-box models use incorrect or confusing variables to achieve results. For instance, a deep learning network trained on asthma patients with active physician input incorrectly inferred a low pneumonia mortality rate. Another deep learning model that screened x-rays for pneumonia used irrelevant information like the scanner’s position. In a third instance, a model for classifying high- and low-risk patients based on x-rays used hardware-related information to predict risk. These examples show that relying just on the models’ reliability is inadequate. There is a need for additional frameworks that foster confidence, such as explainable AI.
Explainable AI in healthcare has also encouraged value-based care, incentivizing providers to focus on outcomes rather than service volumes. For example, XAI algorithms analyze patient data to identify those at higher risk of developing certain conditions. Doing so helps providers to intervene before a health condition becomes severe.
As the use of explainable AI in healthcare expands, we can expect more integration with wearables. Wearables are a rich source of patient data as they monitor patients’ health throughout the day. Explainable AI can tap into this data to formulate suggestions and diagnose accurately.
Benefits of XAI in healthcare
Increased transparency: By explaining how an AI system arrived at a particular conclusion, XAI techniques encourage transparency in healthcare processes. Increased openness and understanding, in turn, generate higher degrees of trust. In addition, the explanations given by XAI techniques can help monitor how components impact the AI system’s ability to predict a result.
Model improvement: AI systems learn from data to generate a forecast. Occasionally, the established rules are incorrect, which may result in inaccurate predictions. Explanations provided by XAI techniques help evaluate the learned rules, allowing mistakes to be recognized and models to be improved.
Improved accuracy: By describing how an AI system arrived at its results, XAI in healthcare can enhance the accuracy of medical diagnoses and treatment regimens. This helps physicians and other medical workers understand why a specific diagnostic plan was suggested, leading to more accurate and successful treatment.
Knowledge transfer: XAI facilitates knowledge transfer from AI systems to medical personnel, who use that knowledge to enhance patient outcomes. For example, an AI system can discover a previously unrecognized relationship between a symptom and a disease. By explaining its rationale, the AI system opens practitioners’ eyes to new connections they can use in future diagnoses.
Early Detection: XAI can aid in the early diagnosis of illnesses by analyzing massive volumes of patient data and discovering patterns that medical practitioners may miss. XAI can also explain why some individuals are more prone to certain diseases, how it reached this conclusion, and suggest preventive measures.
The future of XAI in healthcare
The AI healthcare software business is quickly expanding. According to a report by ResearchandMarkets, the global explainable AI market size is estimated to increase from $3.5 billion in 2020 to $21 billion by 2030 [1]. As the quantity of data created continues to expand, it will be hard to ignore the advantages of AI. However, at the same time, there are valid concerns regarding privacy and security.
In the immediate future, we anticipate government, business, and advocacy organizations to agree on a framework that balances data security with potential benefits. A World Economic Forum report indicates that 2030 will be a pivotal year for AI in healthcare, with numerous new use cases combining to deliver “a genuinely proactive, predictive healthcare system.” [2]
The future of XAI in healthcare also points toward greater data democratization, with individuals having greater control over their data and how it is used.
References
1. Markets Ltd., Research and “Explainable AI Market by Offering, Deployment, Technology, End-Use Industry, and Application—Global Opportunity Analysis and Industry Forecast, 2021–2030. https://www.researchandmarkets.com/reports/5481228/explainable-ai-market-by-offering-by-deployment/
2. Here are 3 ways AI will change healthcare by 2030. (n.d.). World Economic Forum https://www.weforum.org/agenda/2020/01/future-of-artificial-intelligence-healthcare-delivery/
