AI’s black-box reasoning presents some complex challenges in healthcare automation. Fortunately, there are excellent ways to overcome these challenges.
Artificial intelligence continues to pave the way for rapid changes in the healthcare industry. Machine learning improves patient diagnosis, facilitates treatments, and streamlines the entire medical process. But the rapid implementation of AI has led to legitimate concerns regarding black-box reasoning.
The central stigma surrounding AI is the transparency and interdependency of these automated systems. AI’s greatest challenge is bred from the difficulty in understanding how these complex systems calculate data and generate decisions. This article will discuss ways to meet the challenge of AI’s black-box reasoning head-on when developing healthcare automation systems.
AI’s Black Box Reasoning Poses Realistic Risks that Must be Addressed
Since it’s difficult to understand how complex AI systems process, learn, and adapt to such vast amounts of data, there are significant risks that must be addressed. The healthcare industry is built on a foundation of trust and transparency, so it’s easy to see how implementing AI systems can break that foundation. Let’s look at some of the specific risks.
Lack of Interpretability
AI systems utilize complex algorithms that interconnect and adapt as they are fed data. These deep learning networks are challenging to understand, so healthcare professionals need help interpreting the decisions. The concern is that machines are making critical decisions but still need to gain the ability to explain how they came to those conclusions. In critical healthcare scenarios, a lack of interpretability can be dangerous.
Potential Bias and Discrimination
AI bias is a significant challenge to overcome. The problem is that humans input data into the algorithms and decide how that data is applied. Without a diverse team of testers, this biased data is unintentionally entered into AI systems and used to create biased models. Then the AI will automate these biased models, resulting in discriminating behavior.
This makes training data the most valuable aspect of the automation process. A diverse source of individuals must enter it to ensure that machine learning systems get various data to create diverse models.
Safety and Liability
AI’s black-box reasoning limits the ability to understand how or why specific decisions were made. This can lead to errors, which can be severe in the healthcare industry. Due to complexity, we’re still in the early stages of healthcare automation, so machine learning systems are opaque. This presents significant patient safety concerns and liability risks.
Regulatory and Ethical Challenges
Opacity makes it difficult for regulators to ensure that AI systems comply with healthcare regulations. With transparency, decision-makers can ensure that AI systems are fair and that data within these systems is protected. This will corrode public trust as consumer concerns about bias, inaccuracies, and ethical implications continue to grow.
Patient Trust and Acceptance
The lack of transparency in AI’s black-box reasoning will continue to erode patients’ trust. We’ll see this occur frequently in diagnostics. While healthcare automation allows AI systems to analyze vast quantities of medical data to formulate diagnoses, patients cannot understand the rationale behind these treatments. This skepticism eventually hinders future advancements in healthcare automation.
Overcoming the Challenge of AI’s Black Box Reasoning
AI can potentially revolutionize the entire healthcare industry, but specific challenges must be overcome before that can happen. Healthcare automation poses risks if we don’t face the challenge of AI’s black-box reasoning. Fortunately, several strategies have proven successful in meeting this challenge.
Develop Explainable AI Models
Cleaning up training data sets is a great starting point, but the real solution to the need for more transparency is to shift the training approach to a more transparent system. This glass box model utilizes reliable and explainable training data. Users can update and examine this data to build trust and develop a more ethical decision-making process.
Glass box AI systems still make important decisions but do so in a way that can be explained. More importantly, they go through rigorous testing to guarantee accuracy.
Establish Robust Governance Frameworks
AI governance uses various tools and methodologies to manage how an organization uses AI. These guidelines must utilize consistent and exact guidelines in how AI systems will be used. Design, deployment, and monitoring must be laid out in specific, regulated steps to ensure the AI system is ethical.
Governance is broken down into three steps that must be defined:
- Automated capture of information that details how the AI model was developed.
- Accountability checks to ensure that the AI model complies with all rules and regulations set forth by governance guidelines.
- Analytical automation that monitors bias, fairness, and accuracy of the AI model. These analytics must be sharable across the organization.
AI is Transparent While Enhancing Patient Care
AI systems must be transparent and provide accountability for making decisions. Artificial intelligence systems are viewed as transparent when users can interpret and apply their output to their scenario. This means that users should be able to produce detailed reports that explain decisions in a way that can be communicated to patients.
Validating Training Data
Healthcare automation can’t be based on training data alone. Companies are investing heavily in data scientists to create validation data to take machine learning to the next step. Data is split into training and validation sets and then tested for accuracy using test sets.
Validation of training data is essential in creating unbiased, transparent AI systems. It ensures that AI systems return valid and fair predictions.
Outsource to Providers That Already Use Data Validation Practices
Data validation requires specific tools and skilled specialists. As you can imagine, this is expensive to do in-house. Companies worldwide have turned to outsourcing for an affordable solution to make their automated healthcare processes more transparent and ethical.
The best way to ensure your data is adequately validated is to partner with a company like MedBillingExperts that has already implemented automation into its processes. These partners provide a quick way to limit the risks of AI’s black-box reasoning. Since they already use the proper medical coding to be compatible with automated systems, partnering with them is a logical approach.
Contact MedBillingExperts today and let us help you add transparency and eliminate bias from your healthcare automation platform.
