generative artificial intelligence

Overview

Generative artificial intelligence (GenAI) refers to a class of artificial intelligence systems, typically built on large language models and related generative architectures, that can create new content such as text, images, audio, or video in response to prompts or other inputs. In biomedical and health-related settings, GenAI is increasingly used to draft text, summarize information, generate educational materials, support documentation, and assist with decision-making workflows. Its rapid adoption has been driven by the ability of these systems to produce human-like language and other outputs at scale.

From a medical and scientific perspective, GenAI is significant not because it functions as a biological agent, but because it is becoming embedded in clinical, educational, and administrative processes that affect patient care, learner performance, and professional practice. Recent research has emphasized both its utility and its limitations: outputs may be useful for accelerating tasks such as protocol drafting, note generation, and educational content creation, but they require structured human oversight, validation, and attention to ethical, regulatory, and safety concerns. In contexts such as mental health support, clinical documentation, and medical education, GenAI is being evaluated alongside artificial intelligence more broadly for performance, reliability, and risk.

Recent Publications Summary

Recent publications on generative artificial intelligence (GenAI) focused largely on education, workflow support, and implementation considerations in health care. In medical education, a mixed methods workshop for fifth-year medical students integrated GenAI, prompt engineering, clinical reasoning, cognitive-bias awareness, and verification-oriented use centered on ChatGPT, with the aim of improving self-perceived AI literacy and collaborative learning attitudes while examining whether patient-centered orientation changed after intensive AI exposure 42594250Aug. In physiology education, students completed pharmacology-related assignments first with conventional resources and then with GenAI tools, and the study found no significant difference in report scores between the two conditions, although performance was moderately correlated across conditions 42085322May. A separate methodological study developed the ChatGPT Attitude Scale for Nursing Education and reported a two-factor structure—Learning Process Support and Clinical Skill Guidance—with strong internal consistency and good model fit, supporting its use for assessing nursing students’ attitudes toward ChatGPT 42185856May.

Several publications addressed GenAI as a practical aid for clinicians and nursing workflows. A clinical nurse specialist case application described general principles for using GenAI to support evidence-based practice, literature searching, SBAR document creation, and competency tool development, while emphasizing the need for human expert oversight 42555769Aug. An ethical framework paper for machine learning in healthcare used ChatGPT for first-stage drafting of the ETHICS protocol, then refined the output through human source verification, readability optimization, multidisciplinary review, and scenario testing; the authors reported improved readability, strong expert endorsement, and readiness across five clinical scenarios 41795494Mar. Another commentary highlighted the expanding opportunities and challenges of predictive and generative modeling in the life sciences, stressing the need to demystify models and follow best research practices 42397470Jul.

Implementation-focused studies also examined patient and caregiver perspectives and broader outcomes. A qualitative study with patient and caregiver advocates generated prioritized considerations for the design and use of GenAI-supported patient-centered clinical decision support, underscoring the importance of incorporating stakeholder views into co-designed tools 42447289Jul. In public health research, an emulated trial investigated whether GenAI use was associated with subsequent depressive outcomes in a survey of U.S. adults, reflecting concern about possible mental health effects, although the abstract provided here does not report results 42128453May. Across these publications, GenAI was repeatedly framed as a potentially useful technology whose value depends on prompt quality, verification, human oversight, and careful alignment with clinical and educational goals 42555769Aug41795494Mar42594250Aug.

What Changes, What Holds

1. GenAI shows promise in health professions education but does not yet outperform conventional study methods
REINFORCES Medical education uses described here fit the baseline’s account of GenAI as a tool for drafting, support, and educational content rather than as a clinically transformative agent. The student workshop suggests improved AI literacy and collaborative attitudes may be the main educational gains, while the assignment comparison found no score advantage over conventional resources, and the attitude scale adds a measurement tool for nursing education 42594250Aug42085322May42185856May.

2. GenAI can speed practical clinical work, but only as a verified drafting aid under human control
REINFORCES Nurse-facing workflow reports extend the baseline’s emphasis on documentation, protocol drafting, and decision-support assistance without changing the core understanding of GenAI’s role. The ETHICS protocol example and the clinical nurse specialist application both reinforce that useful outputs depend on source checking, readability edits, multidisciplinary review, and expert oversight, so the main advance is operational guidance rather than a new function 41795494Mar42555769Aug.

3. GenAI’s adoption now depends on stakeholder design input and open questions about downstream effects
NEW DIRECTION Patient and caregiver advocates add a role the Overview does not cover: co-design of patient-centered clinical decision support. That widens GenAI’s health-care meaning beyond internal drafting and education to implementation governance. The emulated-trial question about later depressive outcomes also signals concern about possible downstream mental health effects, but the summary gives no results, so the consequence remains unsettled 42447289Jul42128453May.

Overview update candidates: stakeholder-informed co-design for patient-centered clinical decision support; unresolved possible associations with depressive outcomes; expanded implementation guidance for verified; human-supervised clinical drafting.