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Jan Beger

Publications and source records attributed to Jan Beger.

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Three Futures for the Diagnostic Radiologist: A Structured Disagreement About What AI Actually Changes

Rationale. The diagnostic radiologist's role in 2035 will not look like it does today. Imaging AI is already changing how worklists are organized, how reports are generated, and which cases require a radiologist's attention. What remains genuinely contested is not whether the role changes but how. Approach. Three subject-matter experts (two radiologists and one health tech professional with more than 20 years of experience in medical imaging IT) independently authored 2035 job descriptions for the diagnostic radiologist using a shared template. Each author wrote from a distinct vantage point: one optimistic, one framed as a trade-off view incorporating workforce economics, and one structured around professional stratification. The three versions were published openly and subjected to a structured comparison across seven dimensions. Key findings. The three versions agree on direction but disagree on magnitude. All three describe a radiologist whose routine workload is AI-managed, who carries accountability for AI output, and who spends more time on complex cases and clinical collaboration than today's radiologist does. They diverge on headcount, career security, and whether the profession expands broadly, concentrates into a smaller well-compensated group, or stratifies into sharply differentiated tiers. Conclusion. AI won't eliminate the diagnostic radiologist. Whether it expands, concentrates, or stratifies the profession depends on choices health systems haven't made yet. The clinical argument for optimism is real. So is the economic argument for caution. Both can be true simultaneously. Keywords: radiology workforce; artificial intelligence; diagnostic radiology; job redesign; medical imaging IT; AI governance

cs.CY

Not someone, but something: Rethinking trust in the age of medical AI

As artificial intelligence (AI) becomes embedded in healthcare, trust in medical decision-making is changing fast. Nowhere is this shift more visible than in radiology, where AI tools are increasingly embedded across the imaging workflow - from scheduling and acquisition to interpretation, reporting, and communication with referrers and patients. This opinion paper argues that trust in AI isn't a simple transfer from humans to machines - it is a dynamic, evolving relationship that must be built and maintained. Rather than debating whether AI belongs in medicine, it asks: what kind of trust must AI earn, and how? Drawing from philosophy, bioethics, and system design, it explores the key differences between human trust and machine reliability - emphasizing transparency, accountability, and alignment with the values of good care. It argues that trust in AI should not be built on mimicking empathy or intuition, but on thoughtful design, responsible deployment, and clear moral responsibility. The goal is a balanced view - one that avoids blind optimism and reflexive fear. Trust in AI must be treated not as a given, but as something to be earned over time.

cs.CY