Why AI Still Needs Humans in the Loop

Last week, technology strategist Shelly Palmer shared a story that should concern every healthcare leader. He used an AI system to fact-check an article he was writing—a sophisticated system with a thirty-five-page evaluation rubric. The AI verified every claim as accurate. It was wrong about the most important one.

If AI verification requires human oversight for a business article, what does that tell us about healthcare—where errors do not just damage reputations but endanger lives? Continue reading→

Who Owns the Data That Trains Healthcare AI?

In December 2023, The New York Times filed suit against OpenAI and Microsoft, alleging that millions of its articles had been used to train AI models without permission or compensation. The lawsuit joined a growing wave of legal challenges from authors, musicians, and visual artists demanding recognition for their role in creating the data that powers artificial intelligence. Healthcare AI depends on patient data. Those who create the data that trains AI deserve a role in governing its use. Healthcare organizations and their patients now face the same question that media companies have confronted. Who owns the data that trains healthcare AI, and who benefits when that data generates commercial value? Continue reading→

As Federal AI Guardrails Fall, Healthcare Must Build Its Own

Deepfakes are growing more sophisticated. AI generates photographs and videos that we cannot distinguish from those captured by cameras. Social feeds fill with synthetic content mimicking reality with unsettling precision. Industries focused on entertainment, commerce, and social connection already wrestle with misinformation and the need for human guidance. These concerns become even more critical when patients’ lives are at stake. Healthcare AI hallucinations—outputs generated by statistical probabilities but ungrounded in clinical reality—pose much higher risks than a misleading social media post. Continue reading→

From Trust Covenant to Trust Compact in Healthcare AI

Most of AI’s progress has occurred not at the bedside but within the infrastructure of care—largely invisible to patients yet profoundly reshaping how care is delivered. Ambient listening technologies now capture and summarize clinical encounters, reducing the documentation burden that has weighed heavily on physicians for years. Digital assistants and chatbots respond to routine patient questions, manage scheduling, and bridge communication gaps that once led to frustration and inefficiency.

Tools such as OpenEvidence go further, analyzing medical literature in real time to help physicians. Continue reading→

Ethics at Scale: Governing Models That Never Sleep

Every medical innovation before AI—from the stethoscope to magnetic resonance imaging—served as an extension of human skill, amplifying perception while preserving empathy, because the clinician remained entirely in control. Artificial intelligence is different. It shifts part of that control to machines, which make decisions without empathy, moral intent, or lived experience—replacing human judgment with statistical inference. An algorithm can identify patterns, yet only a person can understand what those patterns mean for a human life.

Morals, empathy, and purpose guide humans. Machines are not. They have no sense of duty, no capacity for remorse, and no concept of justice. They execute mathematical rules that describe the world but never experience the weight of moral choice within it. Continue reading→