The Importance of Implementing Explainable AI in Healthcare The Importance of Implementing Explainable AI in Healthcare
Healthcare systems are implementing AI, and patients and clinicians want to know how it works in detail. Explainable AI might be... The Importance of Implementing Explainable AI in Healthcare

Healthcare systems are implementing AI, and patients and clinicians want to know how it works in detail. Explainable AI might be the solution everyone needs to develop a healthier, more trusting relationship with technology while expediting essential medical care in a highly demanding world.

What Is Explainable AI?

Explainable AI (XAI) refers to AI that explains how, where, and why it produces decisions. XAI coincides with white-box models, which detail the results the algorithms have. Most commercially available AI tools are black-box, meaning they do not cite what they generate or make it easy for data scientists to discover where the AI-derived information. Effective XAI follows these main principles:

  • Transparency
  • Understandability
  • Comprehensibility
  • Interpretability
  • Explainability

In a healthcare setting, XAI is essential for providing accurate diagnoses and expediting treatment. It uses data mining techniques like decision trees and rule-based systems to generate correct responses. 

Other AI models offer numerous benefits, but the healthcare sector and its patients expect more accountability and accuracy. XAI can fit this criteria while providing opportunities for innovation and advancement in modern medicine.

What Are XAI Use Cases in Healthcare?

The most apparent and worthwhile use of XAI is in diagnostics, disease detection, ailment prevention, and treatment. It may even assist in detecting comorbidity with never-before-seen accuracy. The speed at which XAI makes determinations allows healthcare professionals and data scientists more room to focus on customized care while the AI does the tedious research. 

XAI encourages a medical industry focused on accurate solutions that meet individual needs instead of trying to treat as many people as fast as possible. This shift in mindset is essential for high-risk patients with notable family histories. Medical professionals can leverage XAI to analyze records to determine preventive, proactive care before issues arise.

These boons apply to all types of testing, including medical imaging. In 2022, AI models successfully detected breast cancer in mammograms from nodules doctors may have deemed inconsequential. 

XAI can look at everything from MRIs to X-rays and explain if a finding is anomalous or characteristic of specific conditions as accurately as human clinicians. Depending on how scientists curate the database, XAI may explain itself against the demographic data it contains, providing more accurate, attentive feedback based on the patient. This level of accuracy is essential for expanding research opportunities, especially for minority communities where healthcare research can be sparse and often unreliable.

What Advancements Will Professionals See Due to XAI?

XAI may also extend into other medtech and wearable devices. With innovations like fitness trackers and smartwatches, healthcare data is ubiquitous. However, other devices like pacemakers or blood sugar monitors would benefit from leveraging data when executing readings and sending information to doctors. 

This type of AI details how patient analytics align with positive or negative trends based on existing research and family history. More importantly, the XAI explains how much each data point influenced its decision for treatment.

Medical researchers will find XAI particularly useful in case studies and niche research. It will catalyze drug discovery and development into a new era. Due to the explainability of XAI, it increases the replicability of medical testing while reducing costs. Novel input is invaluable for training XAI models and reinforcing data stores for consistency and objectivity. 

The more information the AI has, the better it can construct pharmaceutical suggestions and compositions for even more specific cases. Scrubbing and supervising the data will take time, mainly when eliminating bias and addressing anomalies. Regardless, XAI provides an ideal opportunity to overcome challenges because of its inherent transparency.

What Do Healthcare Professionals Gain From XAI?

The medical sector needs the advantages XAI provides. The most big-picture impact medical XAI will have is rewriting the public perception of medical AI. A recent study revealed 40% of healthcare workers and patients alike do not have confidence in medical systems to diagnose and treat patients to their needs accurately. Skepticism increases when hospitals bring AI to the forefront. 

With ethical XAI employment, public opinion shifts in an age of reluctance and misinformation that hinders AI’s potential. It increases trust in AI and healthcare overall. It yields better relationships between patients and medical staff, improving satisfaction and quality of care.

More patients will receive accurate care because of XAI. Its capabilities can transform many medical treatments into proactive prevention rather than reactive treatment. Patients could become healthier and have greater literacy about their health because of the transparency boosts from XAI. The more humanity knows from XAI’s insights, the more likely everyone will remain healthier for longer.

XAI Creates a More Honest and Efficient Healthcare System

The impacts XAI could have on the world’s quality of health are astronomical. It has the potential to become the century’s most revolutionary medical innovation by speeding up treatment and inventing new solutions to ailments that have eluded professionals for decades. 

It is a type of transparency that will hold medical professionals and data scientists to new standards while humanity develops a better relationship with artificial intelligence.

Zac Amos

Zac is the Features Editor at ReHack, where he covers data science, cybersecurity, and machine learning. Follow him on Twitter or LinkedIn for more of his work.