Generative artificial intelligence in healthcare: A scoping review on benefits, challenges and applications

International Journal of Medical Informatics, Год журнала: 2024, Номер 188, С. 105474 - 105474

Опубликована: Май 8, 2024

Язык: Английский

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Generative Pre-Trained Transformer-Empowered Healthcare Conversations: Current Trends, Challenges, and Future Directions in Large Language Model-Enabled Medical Chatbots

BioMedInformatics, Год журнала: 2024, Номер 4(1), С. 837 - 852

Опубликована: Март 14, 2024

This review explores the transformative integration of artificial intelligence (AI) and healthcare through conversational AI leveraging Natural Language Processing (NLP). Focusing on Large Models (LLMs), this paper navigates various sections, commencing with an overview AI’s significance in role AI. It delves into fundamental NLP techniques, emphasizing their facilitation seamless conversations. Examining evolution LLMs within frameworks, discusses key models used healthcare, exploring advantages implementation challenges. Practical applications conversations, from patient-centric utilities like diagnosis treatment suggestions to provider support systems, are detailed. Ethical legal considerations, including patient privacy, ethical implications, regulatory compliance, addressed. The concludes by spotlighting current challenges, envisaging future trends, highlighting potential reshaping interactions.

Язык: Английский

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AI and narrative embeddings detect PTSD following childbirth via birth stories

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Апрель 11, 2024

Abstract Free-text analysis using machine learning (ML)-based natural language processing (NLP) shows promise for diagnosing psychiatric conditions. Chat Generative Pre-trained Transformer (ChatGPT) has demonstrated preliminary initial feasibility this purpose; however, whether it can accurately assess mental illness remains to be determined. This study evaluates the effectiveness of ChatGPT and text-embedding-ada-002 (ADA) model in detecting post-traumatic stress disorder following childbirth (CB-PTSD), a maternal postpartum affecting millions women annually, with no standard screening protocol. Using sample 1295 who gave birth last six months were 18+ years old, recruited through hospital announcements, social media, professional organizations, we explore ChatGPT’s ADA’s potential screen CB-PTSD by analyzing narratives. The PTSD Checklist DSM-5 (PCL-5; cutoff 31) was used CB-PTSD. By developing an ML that utilizes numerical vector representation ADA model, identify via narrative classification. Our outperformed (F1 score: 0.81) previously published large text-embedding models trained on health or clinical domains data, suggesting harnessed modeling approach could generalized other disorders.

Язык: Английский

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Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Media

Research Square (Research Square), Год журнала: 2023, Номер unknown

Опубликована: Ноя. 2, 2023

Abstract In the realm of social media, users frequently convey personal sentiments, with some potentially indicating cognitive distortions or suicidal tendencies. Timely recognition such signs is pivotal for effective interventions. response, we introduce two novel annotated datasets from Chinese focused on and risk classification. We propose a comprehensive benchmark using both supervised learning large language models, especially GPT series, to evaluate performance these datasets. To assess capabilities employed three strategies: zero-shot, few-shot, fine-tuning. Furthermore, deeply explored analyzed models psychological perspective, shedding light their strengths limitations in identifying understanding complex human emotions. Our evaluations underscore difference between approaches, often challenged by subtle category distinctions. While GPT-4 consistently delivered strong results, GPT-3.5 showed marked improvement suicide classification after This research groundbreaking its evaluation media tasks, accentuating models' potential contexts. All code are made available at: \url{https://github.com/HongzhiQ/SupervisedVsLLM-EfficacyEval}.

Язык: Английский

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A preliminary exploration of ChatGPT’s potential in medical reasoning and patient care

Critical Public Health, Год журнала: 2025, Номер 35(1)

Опубликована: Апрель 2, 2025

Язык: Английский

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Integrative modeling enables ChatGPT to achieve average level of human counselors performance in mental health Q&A

Information Processing & Management, Год журнала: 2025, Номер 62(5), С. 104152 - 104152

Опубликована: Апрель 6, 2025

Язык: Английский

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Advantages, Challenges and Prospects of ChatGPT in Oral English Teaching

Transactions on Social Science Education and Humanities Research, Год журнала: 2024, Номер 4, С. 99 - 109

Опубликована: Март 12, 2024

With the development of artificial intelligence, ChatGPT has set off a hot wave in various fields, and brings opportunities challenges to education. The positive negative effects on education have aroused heated discussion academic circles. Based its principle process, continuous optimization language model, can play unique advantage oral English teaching, such as: providing personalized practice, real-time error correction feedback, real dialogue simulation, safe comfortable environment. At same time, challenge teaching is also direction future technological improvement, communication spoken language, recognition voice tone intonation, integration with VR technology, autonomous assessment level, etc., Those become application prospects teaching. Artificial intelligence assisted new mode

Язык: Английский

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GPT-4o and multimodal large language models as companions for mental wellbeing

Asian Journal of Psychiatry, Год журнала: 2024, Номер 99, С. 104157 - 104157

Опубликована: Июль 14, 2024

Язык: Английский

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Children of AI: A Protocol for Managing the Born-Digital Ephemera Spawned by ChatGPT

Опубликована: Июль 31, 2023

The recent public release of the generative AI language model ChatGPT has captured imagination and resulted a rapid uptake widespread experimentation by general academia alike. number academic publications focusing on capabilities as well practical ethical implications been growing exponentially. One concerns with this unprecedented growth in scholarship related to AI, particular ChatGPT, is that most cases raw data, text original ‘conversations,’ have not made available audience papers thus cannot be drawn assess veracity arguments conclusions therefrom. This paper provides protocol for documentation archiving these data.

Язык: Английский

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ChatGPT Demonstrates Potential for Identifying Psychiatric Disorders: Application to Childbirth-Related Post-Traumatic Stress Disorder

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Фев. 26, 2024

Abstract Free-text analysis using Machine Learning (ML)-based Natural Language Processing (NLP) shows promise for diagnosing psychiatric conditions. Chat Generative Pre-trained Transformer (ChatGPT) has demonstrated preliminary initial feasibility this purpose; however, whether it can accurately assess mental illness remains to be determined. This study evaluates the effectiveness of ChatGPT and text-embedding-ada-002 (ADA) model in detecting post-traumatic stress disorder following childbirth (CB-PTSD), a maternal postpartum affecting millions women annually, with no standard screening protocol. Using sample 1,295 who gave birth last six months were 18 + years old, recruited through hospital announcements, social media, professional organizations, we explore ChatGPT’s ADA’s potential screen CB-PTSD by analyzing narratives only. The PTSD Checklist DSM-5 (PCL-5; cutoff 31) was used CB-PTSD. By developing an ML that utilizes numerical vector representation ADA model, identify via narrative classification. Our outperformed (F1 score: 0.82) previously published large language models (LLMs) trained on health or clinical domains data, suggesting harnessed modeling approach could generalized other disorders. 1

Язык: Английский

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