More Than Codes: The Ethical Dilemma Surrounding AI in Healthcare

Paris, France – As artificial intelligence rapidly integrates into healthcare systems around the world, a growing chorus of voices is calling for a fundamental ingredient too often overlooked in the rush toward innovation: trust.

At a recent international panel on AI in healthcare, experts from law, ethics, medicine, and data science agreed on one thing: the promise of AI will only be realized if patients and clinicians alike can trust how it's developed, deployed, and explained.

"Everyone wants transparency," said Artur Olesch, a health journalist and founder of Health Algorithms, opening the discussion. "But not all transparency is useful — or even possible."

Transparency: Between Ethics and Implementation

That paradox sits at the heart of the debate. Noemi Conditi, a lawyer and research fellow at the University of Bologna, argued that while transparency is critical for building trust, it must be thoughtfully balanced with other priorities.

"How much do we want to know about how AI systems work?" she asked. "More transparency can mean less intellectual property protection. And on the patient side, too much detail can overwhelm or confuse. But without any transparency, how can we trust the system at all?"

Dr. Jessica Morley of Yale University's Digital Ethics Center added a cautionary note about superficial efforts. "Transparency has become a tick-box exercise. Just putting a privacy statement on a website doesn't mean you're actually being transparent. It has to be meaningful."

The Need for Auditability, Not Just Explainability

Rather than demanding a line-by-line explanation of how an algorithm works — a technical impossibility in many deep-learning systems — Morley emphasized the need for "auditability": clear documentation of who built the model, with what data, how it was trained, and how it's monitored in real time.

That sentiment was echoed by Professor Eric Wong, Group Chief Digital Health Officer in Singapore's National Healthcare Group. "Expecting full transparency is like asking for a randomized control trial on parachutes," he said. "Some things just work. But that doesn't mean we skip safeguards."

Wong described Singapore's approach: always deploying AI with human oversight, and ensuring clinicians remain in the loop. "Studies show that algorithms sometimes outperform doctors — and vice versa. But together, they do better than either alone."

Bias: The Unseen Clinical Risk

Panelists also tackled the critical issue of bias — how it creeps into algorithms, and how much of it healthcare systems can tolerate.

"You'll never have a completely unbiased model," Morley said. "Even our drugs aren't unbiased. What we need is to know where the bias is, and how it might affect patients."

For Steffen Hess, head of the Health Data Lab at Germany's BfArM, transparency about training data is key. "If an algorithm is trained on biased data, it will make biased decisions. We must make that process visible, not just the algorithm itself."

The writing has been enhanced using AI tools but the content and subject topic is 100% human and the work of credited individual.