Artificial Intelligence (AI) in Healthcare: Ensuring Trust, Equity, and Transparency
Dr. Pablo Moreno Franco is an intensivist in Jacksonville. Dr. Moreno Franco ensures the safety of patients of critically ill patients. Intensivists, also known as critical care physicians, play a crucial role in the care of critically ill patients, particularly in the intensive care unit (ICU). They are board-certified... more
Imagine a scenario where a voice AI assistant alerts a doctor about a patient showing signs of sepsis. This isn't just about the AI being accurate; it's about trust. Trust in the AI, the data it was trained on, the team that built it, and the system that deployed it. Trust in AI is not something you can just plug in; it's a process built on fairness, transparency, collaboration, and continuous learning.
Trust in AI isn't just about getting the numbers right. It's about making sure AI fits into how people work and think. This means it needs to work well with real-life workflows, communicate clearly, anticipate problems, and be fair and transparent. One tool to help with this is the Team Card, which documents who is on the team, their assumptions, and design decisions to address unconscious bias. Bias in AI isn't just about biased data. It includes institutional bias and how the AI is designed. For example, an algorithm that uses health spending as a proxy for health needs might underestimate the need for care among certain groups, such as patients from diverse backgrounds. There are risks to letting AI make clinical decisions. These include data bias, the AI being a "black box" (not understanding how it works), overreliance on AI, and access inequity. AI systems need to be explainable, auditable, and accountable to humans. Healthcare providers are being educated on digital literacy, how to interpret AI, ethics, fairness, and team-based learning. It's important to include emotional, ethical, and interpersonal aspects in this education.AI needs to be contextualized globally, considering infrastructure, language, regulation, and cultural expectations. Community engagement and open-source, inclusive, and reproducible innovation are key.AI will change how clinicians work, focusing more on communication, empathy, and shared decision-making. Trust must be continually earned through transparency, collaboration, and design justice. In conclusion, building trust in AI is a continuous process that involves challenging assumptions and centering people in AI development. Trust is built with intention, not just code. Ethical considerations are crucial in AI development and deployment. Here are some key principles:
- Equity and Fairness: AI should ensure equitable access to healthcare and avoid existing biases.
- Transparency and Explainability: AI systems should be clear in their decision-making processes.
- Privacy and Confidentiality: Protecting patient data is essential.
- Accountability and Responsibility: Developers and healthcare providers must be accountable for AI outcomes.
- Informed Consent: Patients should be informed about AI use in their care.
- Human-Centered Design: AI should be designed with the needs and values of patients and healthcare providers in mind.
