SKU/Artículo: AMZ-B0GMD2RY7L
GENERATIVE AI IN THE CLINIC: Ambient Documentation Systems
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Paperback
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0.56 kg
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Amazon
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USA
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- Generative AI in the Clinic arrives at precisely the moment medicine needs it most—after adoption has begun, but before consequences have fully surfaced. In a landscape crowded with breathless predictions and defensive reassurances, Dr. Alexander Vance offers something rarer and more durable: a clear-eyed examination of how generative AI, when embedded into clinical documentation, quietly reshapes responsibility itself. This is not a book about fear, nor is it a book about technological triumph. It is a book about structure. Vance writes as a physician who understands the lived realities of clinical work, as a systems thinker who recognizes how incentives and workflows drift, and as a careful reader of legal history who knows what happens when governance arrives late. His central insight is both simple and unsettling: when machines participate in writing the medical record, authorship becomes ambiguous—and ambiguity, at scale, is risk.What sets this book apart is its refusal to argue at the level of abstraction. Rather than debating “AI in healthcare” in general terms, Vance focuses on a specific and consequential practice: ambient documentation systems that listen to clinician–patient conversations and generate the medical note. These tools promise relief from documentation burden, and often deliver it. But they also introduce a second author at the point where clinical reality becomes institutional truth. The note, once generated, does not remain local. It travels—to other clinicians, to insurers, to regulators, to courts. Errors do not announce themselves as errors; they appear as plausible narrative. Drawing on emerging empirical evidence showing that ambient documentation is already widespread across U.S. hospitals, Vance makes clear that this is no longer a hypothetical concern. Adoption has moved beyond pilots and into infrastructure. A small number of vendors dominate the market. Deployment correlates with workload pressure and financial capacity. Teaching hospitals no longer stand apart from community systems. In short, the conditions for diffusion are already in place—while the safeguards remain uneven. The book’s intellectual backbone is a sober comparison to the cautionary tale of Section 230. Vance does not argue that medicine is repeating the same mistake, but that it is flirting with the same pattern: early adoption under the banner of assistance, followed by the diffusion of responsibility, followed—inevitably—by litigation once harms become visible. Governance delayed, he reminds us, does not disappear; it is reconstructed later by courts, bluntly and at great cost. Yet Generative AI in the Clinic is not a counsel of despair. Its most compelling contribution is constructive. Vance proposes a framework for governance that preserves innovation while restoring clarity: separating machine drafts from authoritative records, insisting on meaningful consent, requiring auditability and change control, and preserving practical correction rights for patients. His concept of the “Second Document”—a parallel, explicitly non-authoritative AI record—offers a way to gain the benefits of generative tools without surrendering authorship by default. Stylistically, the book is restrained and confident. Vance writes without polemic, trusting the reader to follow careful reasoning rather than rhetorical flourish. His prose reflects clinical discipline: precise, humane, and unsentimental. The moral force of the book lies not in outrage, but in foresight. This is a book for clinicians who sense that something important has shifted but lack the language to name it; for administrators balancing operational pressure against long-term institutional risk; for policymakers and regulators searching for governance that can arrive before crisis; and for anyone concerned with how societies decide who is responsible when machines speak in our voice.
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