The term “black box” in AI means that the computer models are very complex and hard to understand. These AI systems use deep learning with many layers and connections. Even doctors and nurses might find it hard to explain how a certain AI result was made.
In hospitals and clinics, this confusion can make patients trust their doctors less. Heather Cox, a content manager at Onspring, says if the AI is not clear, patients may not believe or follow their treatment plans. When doctors cannot explain how AI gave a diagnosis or treatment, patients might not accept it. This could lead to worse health.
Algorithmic transparency has three parts:
Healthcare places need to make sure their AI meets these rules so patients can trust it and care stays good.
Doctors and hospitals in the US must deal with tough issues about who is responsible for AI decisions. Because AI often works like a black box, it is hard to see how advice or recommendations were made or where errors happened. This makes it tricky to say who is legally responsible, especially if a patient is harmed.
Unlike usual medical tools, AI can learn and change over time. Designers, doctors, and users may share responsibility. Research, including a study from the European Parliament, suggests laws might be needed to hold AI creators responsible for major errors. Another idea is to have special funds that pay patients without long legal fights, as suggested by the World Health Organization (WHO).
In the US, the Food and Drug Administration (FDA) has approved over 1,200 AI tools for medical use. The FDA watches these devices to keep patients safe. But since AI changes fast, hospital leaders must keep up with the latest FDA rules and follow them closely.
Not dealing with the black box problem can cause serious risks such as:
Steve Lefar, CEO of Applied Pathways, warns that saying AI is better than it really is can cause big risks, even life-or-death problems in healthcare.
AI uses past healthcare data to learn, but this raises worries about bias and unfair results. The data can show unfair treatment from society, and AI may copy that. For example, some studies found AI risk scores give lower risk to Black patients than White patients, not because of real health, but because of indirect data tied to cost differences. Also, AI for skin disease checks makes more mistakes with darker skin because it was trained mostly on lighter skin samples.
These biases can hurt fair care and make health differences worse. They also cause ethical problems about fairness under US civil rights laws. Healthcare leaders should check AI tools regularly for bias and work with vendors who share clear test results.
Rules for AI in healthcare are changing. Other countries like the EU have new AI laws coming soon, but the US still uses older laws such as HIPAA, FDA rules, and general tech laws.
In California, the law AB 3030 says doctors must tell patients when AI is used to help with medical decisions. This is one of the first US steps to make AI use more open to patients and may become common across the country.
Following these rules is important when using AI in healthcare. This means protecting patient data, keeping records about AI use, checking risks, and training workers about ethical AI use and new workflows. Heather Cox from Onspring suggests creating clear ethical rules about data bias, how AI is made, and how people work with AI results.
The FDA watches AI medical devices even after approval, checking how well they work and if they stay safe. Hospitals should also pick AI partners who follow tough rules and audits, like the NIST AI Risk Management Framework (AI RMF), which controls AI accuracy, fairness, and following laws.
Apart from helping doctors, AI can also automate office work in clinics and hospitals. Companies like Simbo AI use AI to answer phone calls and help with scheduling and insurance. This can save time for staff and improve patient experience by cutting wait times and giving fast replies.
But using AI for office work also has issues like transparency and being responsible. For example, an AI phone system must work well and keep patient information safe according to HIPAA rules.
Healthcare leaders must check AI automation tools on:
When done right, AI automation can make operations smoother without lowering healthcare quality.
Using AI successfully in healthcare needs support from leaders and medical staff at all levels. Soyal Momin, Vice President of Data and Analytics at Presbyterian Healthcare Services, says it helps to pick clear goals and start with small test projects instead of trying everything at once. This helps the group learn and get ready.
Hospitals should check how good they are at data and find what is missing before using AI widely. Good change management including training and adjusting workflows is key to help workers use AI properly.
A way to reduce problems with black box AI is to have humans watch the AI results. Doctors and staff should check AI advice before using it for patient care. Explainable AI (XAI) is a research area that tries to make AI more clear and easier to understand.
With explainable AI, doctors can know why AI gave some advice, find any bias, and talk about it with patients. This helps doctors keep responsibility and lets patients understand and agree to their care.
Using AI in healthcare across the US can help with better care, smoother operations, and more patient involvement. Still, the black box problem in many AI tools brings challenges for managers, owners, and IT staff about being responsible, legal risks, following rules, and ethics.
Careful and clear approaches based on rules, ethics, involving all people, and human checks can help hospitals handle these challenges. Doing this keeps patients safe and trusting, while still benefiting from AI in healthcare.
Identifying a defined use case with a need for improvement is crucial. This allows organizations to experiment and iterate in a limited environment before broader implementation.
Organizations should assess where they lie on the maturity curve of analytics and ensure they have foundational competencies before moving forward with advanced AI initiatives.
Engaging stakeholders at all levels, including clinical and executive leaders, is vital for building buy-in, addressing concerns, and facilitating effective workflow changes.
Organizations must evaluate vendors for transparency and results, focusing on the actual capabilities of the technology rather than the hype surrounding AI.
Using a mix of process measures and outcomes measures can help gauge improvements, such as patient satisfaction, provider efficiency, and clinical outcomes.
Change management is crucial to ensure that technology is effectively integrated, which involves training staff and optimizing workflows for long-term value.
Establishing a data governance team ensures that data definitions are clear and trusted, which is essential for accurate monitoring and decision-making.
Organizations should adopt an agile implementation approach, allowing them to iterate quickly, gather feedback, and optimize processes based on real-world insights.
Black box AI tools can obscure their analytic processes, complicating verification and raising concerns about accountability and legal implications if outcomes are adverse.
For financial goals, organizations may track KPIs related to revenue fluctuations, fraud rates, claims clearance, and documentation quality to assess AI’s impact.
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