An industry, such as healthcare and health-tech, where mistakes in decision making can cause fatalities, requires technology that can be trusted and audited. As healthcare leads the way into implementing AI technology, explainable AI can provide the analytical, operational and supplemental support necessary to supercharge the adoption of AI systems. Adding trust and transparency into blackbox machine learning models: xAI platforms are well-positioned to revolutionize the way we operate in healthcare.
The global AI healthcare market is predicted to reach $8 billion by 2026 growing at a CAGR of 49%. COVID-19 has been a catalyst in bringing AI to the center of our healthcare sector. From robot-assisted surgeries to virtual diagnostics, AI is revolutionizing the healthcare sector significantly.
In many parts of the world, telemedicine is now making healthcare more accessible; in essence, it is the “technology” or an AI assistant that interacts with the patients and helps patients with cheaper but quality healthcare. Many telemedicine providers are actively deploying algorithms to improve their patient experience. Following their lead, hospitals are also actively deploying AI models to reduce the amount of hospital stay and improve the quality of diagnosis. These improvements in operational efficiencies help the hospital reduce its overall costs in terms of money and time.
As medical care providers adopt and deploy machine learning models in a decision-making capacity, we see a gradual push for the integration of explainability in today’s black-box AI models due to the sensitive nature of the field. Concerns have been raised about inaccuracies within AI systems leading to fatal mistakes. We have some high profile failures, especially when Google medical AI that was supposed to help nurses take photos of patients eyes during the check-up and get an accurate diagnosis results in less than 10 mins failed. Despite the fact the algorithm was super accurate in the lab, real life turned out to be a failure - nurses felt frustrated when they believed the rejected scans showed no signs of disease and the follow-up appointments were unnecessary. This experience tells us how critical explainable AI is to make AI work in the real world.
Let’s look at another detailed example. For a doctor to use AI to predict heart disease given a patient’s records poses the need for transparency. The doctor wants to better understand how the model works, how it arrived at the prediction, what variables were playing an important role in influencing the outcome and what recommendations can be made to help patients mitigate the effects of this heart disease.
The role of explainability becomes even more crucial when stakes are high. It brings us to understand the importance and urgency of adoption of explainable AI tools.
On the outset, it should be quite clear that explainable AI is here to bridge the trust gap between AI technology and its consumers, for example, doctors or healthcare professionals. By deploying explainable AI tools and techniques, model consumers can get justifications for their results in a format that humans can understand. They can understand the rationale behind each recommendation and further validate automated decisions. Hospitals can improve their operational efficiencies and provide better cheaper healthcare through the use of AI medical assistants - this is only possible if we have enough trust in the models powering these AI assistants - luckily, it is achievable through the use of explainable AI tools & techniques.
Explainable AI is also vital for the model developer (data scientist or a machine learning engineer) who can use explainable AI tools to understand and debug their machine learning models. The idea is to ensure they can explain their results to the doctor (before deploying these algorithms) and make sure the AI predictions are unbiased and logically sound. It automatically leads to better development of machine learning models and more robust AI systems.
In a nutshell, the healthcare ecosystem can advance with the adoption of explainable AI tools & techniques. Healthcare companies need to act fast and invest their resources in building AI systems that are explainable, trustworthy and extremely effective: paving the way for the next generation of healthcare.
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