Title Page

Taber’s Cyclopedic Medical Dictionary, 25th Edition
Copyright © 2025 F. A. Davis Company. All rights reserved.

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Editor

Donald Venes, M.D., M.S.J., F.A.C.P.

Introduction

INTRODUCTION TO THE 25TH EDITION

During our long slog through Covid it seemed to many health care providers that we might never recover our mojo. And yet somehow, with courage, dedication, and humor, we did.

Many of us—allied health professionals, nurses, and doctors; educators and practitioners—wondered if our professional lives would ever return to normal. The answer, as you might expect has been both “yes” and “no.”

Yes, we are back at it, applying our hearts and minds to the problems that affect the health of our communities the most. And no, time hasn’t stood still. The way we practice health care today—and the ways we communicate with patients about their health—has been both re-imagined and refreshed. When the pandemic waned new vistas appeared, including the promise of employing previously unforeseen ways of approaching our work. Innovation has given birth to the rise of new insights into people and their understanding of health, and has spawned new ways of using computer science, information technology, and robotics to achieve better medical care.

As this edition of Taber’s goes to press, artificial intelligence (AI)—in development for decades—now seems on the verge of transforming our lives. Some forms of AI can already compose documents, like empathic-sounding letters to patients in response to their questions, and AI is beginning to help us see many details in radiological images that were previously obscure. When it is trained on large data sets it can do some things that seem astounding. In one older, well-publicized study, after training on thousands of images of grazing cattle, an AI program was able to identify other images that contained cows. But there was a hitch: if the software was presented with images of cows on beaches instead of
pastures, it couldn’t see them. The training image set showed cows in a more familiar setting: green, grassy fields, and the program absorbed this information and mistakenly identified the background as crucial to the concept of “cow.” Even after years of work in the field, AI’s ability to deliver on some of its most hyped functions—to extrapolate from data and think critically about it, remains a long way off. In that sense, AI may be misnamed. At this stage in its development it seems less than fully intelligent, and more a tool that digests the hay that’s fed to it and like a cow, regurgitates it.

If AI is not yet creative, thoughtful, or terribly good at separating signal from noise, it may soon reach an inflection point. If it does, what functions might it provide to patients, health care professionals and health systems?

In image analysis, software might meticulously examine visual details. It might identify suspicious micro calcifications in mammograms, for example, or significant, embedded details in echocardiograms. It may be able to look at pictures of rashes or moles and suss out features that appear benign or malignant.

In diagnosis and patient management—it is hoped that AI will identify individuals (based on their presenting signs, symptoms or laboratory data for example) who need hospital admission or ICU care, as opposed to, for example, discharge to outpatient follow-up. By identifying what’s numerically linked to outcomes, and what is not, it may advance the way we triage the injured and the ill.

In pharmacology it may point to new chemical structures that lead to new drug development, while improving our understanding of how therapies can be safely used in specific patient populations, but not in others.

In health care documentation, its natural language processing elements should lead to enhanced documentation of clinical encounters.

In health care administration it may point to other inefficiencies in resource allocation.

And it is hoped that it may help generate new best practices. To take a simple example, imagine if AI could analyze the charts of tens of thousands of people with common injuries, like strains or sprains. Currently there is significant practice variation in how these injuries are managed. Some practitioners may evaluate them with detailed physical examination. Other may get x-rays on every patient who twists an ankle. Treatments offered to people with sprains also vary widely: some practitioners recommend rest, cold packs and splinting, others—non-steroidal anti-inflammatory medications, and yet others—injections or referrals to physical therapists or Orthopedic specialists. Which of these approaches currently helps patients the most? Which has the best outcomes over the short term, and the long? Which makes the most patients healthier, quicker, at the lowest cost? Could AI provide the information we need to select one approach for most individuals and reject the others as inferior? Could it personalize recommendations? Could it help provide a decision support tool—if a sprain has these features—then do this, or, if it has those same or other elements—avoid that? Could AI identify regional or occupational differences in the way we approach patient’s problems? Could it lead to the dissemination of protocols that enhance or replace what is currently being done haphazardly? Could it do the same in cancer care, or respiratory illnesses, or in the management of mental health crises?

Perhaps most important: Could AI help us communicate better with patients? Could it improve our skills as patient educators by showing us ways to provide accurate and comprehensive information that is not only more understandable than what we’ve provided in the past, but also more persuasive? Augmenting the way we communicate could, in theory, lead to a revolution in health care that is not just computationally precise and rational, but also more humane.

The foundation of knowledge underpinning every health professional’s understanding of care is built upon several core sciences: anatomy, physiology, biochemistry, cell biology, and even computer science (to name a few), and also upon pattern recognition—the relative weighting of the signs, symptoms, language, and feelings of the patients who come seeking our help. At F.A. Davis our aspiration for this 25th edition of Taber’s is to provide you with an encyclopedic medical dictionary you can use to enter this textured and rapidly changing world, and to understand it well enough to use the innovations of the future in meaningful context.

Donald Venes, M.D., M.S.J., F.A.C.P.
Brookings, Oregon
April 7, 2024
For April

Abbreviations Used in Text

Notices and Disclaimers