Generative AI’s healthcare professional role creep: a cross-sectional evaluation of publicly accessible, customised health-related GPTs DOI Creative Commons
Benjamin Chu, Natansh D. Modi, Bradley D. Menz

et al.

Frontiers in Public Health, Journal Year: 2025, Volume and Issue: 13

Published: May 9, 2025

Introduction Generative artificial intelligence (AI) is advancing rapidly; an important consideration the public’s increasing ability to customise foundational AI models create publicly accessible applications tailored for specific tasks. This study aims evaluate accessibility and functionality descriptions of customised GPTs on OpenAI GPT store that provide health-related information or assistance patients healthcare professionals. Methods We conducted a cross-sectional observational from September 2 6, 2024, identify with functions. searched across general medicine, psychology, oncology, cardiology, immunology applications. Identified were assessed their name, description, intended audience, usage. Regulatory status was checked U.S. Food Drug Administration (FDA), European Union Medical Device Regulation (EU MDR), Australian Therapeutic Goods (TGA) databases. Results A total 1,055 customised, targeting professionals identified, which had collectively been used in over 360,000 conversations. Of these, 587 psychology-related, 247 105 52 30 immunology, 34 other health specialties. Notably, 624 identified included professional titles (e.g., doctor, nurse, psychiatrist, oncologist) names and/or descriptions, suggesting they taking such roles. None FDA, EU MDR, TGA-approved. Discussion highlights rapid emergence accessible, GPTs. The findings raise questions about whether current medical device regulations are keeping pace technological advancements. results also highlight potential “role creep” chatbots, where begin perform — claim functions traditionally reserved licensed professionals, underscoring safety concerns.

Language: Английский

Artificial Intelligence in Medical Care – Patients' Perceptions on Caregiving Relationships and Ethics: A Qualitative Study DOI Creative Commons

Jana Gundlack,

Sarah Negash,

Carolin Thiel

et al.

Health Expectations, Journal Year: 2025, Volume and Issue: 28(2)

Published: March 17, 2025

ABSTRACT Introduction Artificial intelligence (AI) offers several opportunities to enhance medical care, but practical application is limited. Consideration of patient needs essential for the successful implementation AI‐based systems. Few studies have explored patients' perceptions, especially in Germany, resulting insufficient exploration perspectives outpatients, older patients and with chronic diseases. We aimed explore how perceive AI focusing on relationships physicians ethical aspects. Methods conducted a qualitative study six semi‐structured focus groups from June 2022 March 2023. analysed data using content analysis approach by systemising textual material via coding system. Participants were mostly recruited outpatient settings regions Halle Erlangen, Germany. They enrolled primarily through convenience sampling supplemented purposive sampling. Results Patients ( N = 35; 13 females, 22 males) median age 50 years participated. mixed socioeconomic status affinity new technology. Most had Perceived main advantages its efficient flawless functioning, ability process provide large volume, increased safety. Major perceived disadvantages impersonality, potential security issues, fear errors based staff relying too much AI. A dominant theme was that human interaction, personal conversation, understanding emotions cannot be replaced emphasised need involve everyone informing about considered as responsible decisions applications. Transparency use protection other important points. Conclusions could generally imagine support care if usage focused well‐being relationship maintained. Including development adequate communication systems are practice. Patient or Public Contribution Patients' perceptions participants this crucial. Further, assessed presentation comprehensibility research during pretest, recommended adaptations implemented. After each FG, space provided requesting modifications discussion.

Language: Английский

Citations

1
Regulation of Health and Health Care Artificial Intelligence DOI
Michelle M. Mello, I. Glenn Cohen

JAMA, Journal Year: 2025, Volume and Issue: unknown

Published: March 17, 2025

This Viewpoint discusses prospects for populating the regulatory landscape health and care AI in coming years.

Language: Английский

Citations

1
Integrating Artificial Intelligence into Causal Research in Epidemiology DOI Creative Commons
Ellicott C. Matthay, Daniel B. Neill, Andrea R. Titus

et al.

Current Epidemiology Reports, Journal Year: 2025, Volume and Issue: 12(1)

Published: March 24, 2025

Language: Английский

Citations

1
Is regulatory science ready for artificial intelligence? DOI Creative Commons
Thomas Härtung,

Maurice Whelan,

Weida Tong

et al.

npj Digital Medicine, Journal Year: 2025, Volume and Issue: 8(1)

Published: April 10, 2025

Abstract Trust is key in AI for regulatory science, but its definition debated. If models use different features yet perform similarly, which should be trusted? scientific theories must testable, how critical explainability? At the Global Summit on Regulatory Science (GSRS24), regulators agreed that successful adoption requires ongoing dialogue, adaptability, and AI-trained personnel to harness potential responsibilities evolving 21st-century landscape.

Language: Английский

Citations

1
Recommendations to Ensure Safety of AI in Real-World Clinical Care DOI
Dean F. Sittig, Hardeep Singh

JAMA, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 27, 2024

This Viewpoint provides recommendations for health care organizations (HCOs) and clinicians to facilitate the use of artificial intelligence (AI)–enabled systems, including electronic records with AI features, in routine clinical pragmatic guidance HCOs at all stages implementation.

Language: Английский

Citations

6
Shear Flow Deformability Cytometry: A Microfluidic Method Advancing Towards Clinical Use-A review DOI Creative Commons

Lija Fajdiga,

Špela Jokhadar Zemljič,

Tadej Kokalj

et al.

Analytica Chimica Acta, Journal Year: 2025, Volume and Issue: 1355, P. 343894 - 343894

Published: March 4, 2025

Shear flow deformability cytometry is an emerging microfluidic technique that has undergone significant advances in the last few years and offers considerable potential for clinical diagnostics disease monitoring. By simultaneously measuring mechanical morphological parameters of single cells, it a comprehensive extension traditional cell analysis, delivering unique insight into deformability, which gaining recognition as novel biomarker health disease. Due to its operating principle, method particularly suitable analysis blood samples. This review focuses on recent developments shear cytometry, widely adopted variant cytometry. It strong applications practice due robust simple operation, demonstrated with whole samples, well high throughput, can reach approximately 1000 cells per second. We begin by discussing some basic factors influence properties give overview operational principles samples from blood, cultured tissues. Next, we clinically relevant cancer cells. Finally, address key challenges adoption, such regulatory approval, scalable manufacturing, workflow integration, emphasizing need further validation studies facilitate implementation. article uniquely emphasizes relevance giving biomarkers studied In addition, addresses critical barriers translation. identifying these obstacles, this aims demonstrate bridge gap between research routine medical practice.

Language: Английский

Citations

0
Regulation of AI: Learnings from Medical Education DOI
Kerstin Noëlle Vokinger, Derek Soled, Raja-Elie E. Abdulnour

et al.

NEJM AI, Journal Year: 2025, Volume and Issue: 2(5)

Published: April 24, 2025

Language: Английский

Citations

0
The application of artificial intelligence-based tools in the management of hepatocellular carcinoma: current status and future perspectives DOI Open Access
Ciro Celsa, A. Quartararo, Marcello Maida

et al.

Hepatoma Research, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 9, 2025

Artificial intelligence (AI) is rapidly advancing in hepatocellular carcinoma (HCC) management, offering promising applications across diagnosis, prognosis, and treatment. In histopathology, deep learning models have shown impressive accuracy differentiating liver lesions extracting prognostic information from tissue samples. For biomarker discovery, AI techniques applied to multi-omics data identified novel signatures predictors of immunotherapy response. radiology, convolutional neural networks demonstrated high performance classifying hepatic lesions, grading tumors, predicting microvascular invasion computed tomography (CT) magnetic resonance imaging (MRI) images. Multimodal integrating genomics, clinical are emerging as powerful tools for risk stratification. Large language (LLMs) show potential support decision making patient education, though concerns about remain. While holds immense promise, several challenges must be addressed, including algorithmic bias, privacy, regulatory compliance. The successful implementation HCC care will require ongoing collaboration between clinicians, scientists, ethicists. As technologies continue evolve, they expected enable more personalized approaches potentially improving treatment selection, outcomes. However, it crucial recognize that designed assist, not replace, expertise. Continuous validation diverse, real-world settings essential ensure the reliability generalizability care.

Language: Английский

Citations

0
CORE-MD clinical risk score for regulatory evaluation of artificial intelligence-based medical device software DOI Creative Commons
Frank Rademakers, Elisabetta Biasin, Nico Bruining

et al.

npj Digital Medicine, Journal Year: 2025, Volume and Issue: 8(1)

Published: Feb. 6, 2025

The European CORE–MD consortium (Coordinating Research and Evidence for Medical Devices) proposes a score medical devices incorporating artificial intelligence or machine learning algorithms. Its domains are summarised as valid clinical association, technical performance, performance. High scores indicate that extensive investigations should be undertaken before regulatory approval, whereas lower which less pre-market evaluation may balanced by more post-market evidence.

Language: Английский

Citations

0
Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review DOI
Avirup Guha, Viraj Shah,

Tarek Nahle

et al.

Current Cardiology Reports, Journal Year: 2025, Volume and Issue: 27(1)

Published: Feb. 19, 2025

Language: Английский

Citations

0