ChatGPT as a Way to Enhance Parents’ Communication in Cases of Oncological Pediatric Diseases

Prazeres, Filipe

doi:10.4274/tjh.galenos.2023.2023.0326

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…comparison against clinician performance at various tests, b.) summarization tasks, c.) question and answering tasks for patient or clinician questions or recommendations[30–34]. Hence, publications related to comparison between clinician experts’ performance on proficiency tests such as board examinations were significant[35,36].…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Healthcare: 2023 Year in Review

Awasthi,

Mishra,

Grasfield

et al. 2024

Preprint

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Background: The infodemic we are experiencing with AI related publications in healthcare is unparalleled. The excitement and fear surrounding the adoption of rapidly evolving AI in healthcare applications pose a real challenge. Collaborative learning from published research is one of the best ways to understand the associated opportunities and challenges in the field. To gain a deep understanding of recent developments in this field, we have conducted a quantitative and qualitative review of AI in healthcare research articles published in 2023. Methods: We performed a PubMed search using the terms, machine learning or artificial intelligence and 2023, restricted to English language and human subject research as of December 31, 2023 on January 1, 2024. Utilizing a Deep Learning-based approach, we assessed the maturity of publications. Following this, we manually annotated the healthcare specialty, data utilized, and models employed for the identified mature articles. Subsequently, empirical data analysis was performed to elucidate trends and statistics.Similarly, we performed a search for Large Language Model(LLM) based publications for the year 2023. Results: Our PubMed search yielded 23,306 articles, of which 1,612 were classified as mature. Following exclusions, 1,226 articles were selected for final analysis. Among these, the highest number of articles originated from the Imaging specialty (483), followed by Gastroenterology (86), and Ophthalmology (78). Analysis of data types revealed that image data was predominant, utilized in 75.2% of publications, followed by tabular data (12.9%) and text data (11.6%). Deep Learning models were extensively employed, constituting 59.8% of the models used. For the LLM related publications,after exclusions, 584 publications were finally classified into the 26 different healthcare specialties and used for further analysis. The utilization of Large Language Models (LLMs), is highest in general healthcare specialties, at 20.1%, followed by surgery at 8.5%. Conclusion: Image based healthcare specialities such as Radiology, Gastroenterology and Cardiology have dominated the landscape of AI in healthcare research for years. In the future, we are likely to see other healthcare specialties including the education and administrative areas of healthcare be driven by the LLMs and possibly multimodal models in the next era of AI in healthcare research and publications.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Healthcare: 2023 Year in Review

Awasthi,

Mishra,

Grasfield

et al. 2024

Preprint

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Can Artificial Intelligence Assist Nurses in Planning the Nursing Care of a Child with Acute Lymphoblastic Leukemia?

Aközlü,

Erkut

2024

Indian J Med Paediatr Oncol

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Background Today, the rapid development of artificial intelligence (AI) based technologies and their widespread use in the health sector offer important opportunities in the field of nursing practices and patient care. Therefore, there is a need for research to better understand and evaluate the impact of AI-based applications on nursing. In this study, we aimed to determine and evaluate the nursing care practices planned by AI for a pediatric case diagnosed with acute lymphoblastic leukemia. Methods Within the scope of the study, a hospitalization scenario for a child diagnosed with acute lymphoblastic leukemia was created by the researchers in line with the literature. The scenario and five open-ended questions were directed to ChatGPT (OpenAI), an AI application. The responses were evaluated in line with the literature. Results It was determined that AI did not include the measurement of vital signs in the planning of nursing care for the current problems of the child diagnosed with acute lymphoblastic leukemia, and could not detect anemia, thrombocytopenia, alopecia, and nausea/vomiting among the possible problems of the child. Conclusion Although it is thought to address the patient in a multidimensional way with its responses, the knowledge, experience, and equipment of the nurse are needed to filter the information provided by AI. In line with the data obtained, it is recommended that nurses make a final assessment for the appropriateness of the intervention when deciding to follow an AI-based recommendation.

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Letter to the editor: Evaluating ChatGPT as a tool for educating caregivers on malaria vaccination for children

Prazeres

2024

Clin Exp Vaccine Res

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ChatGPT as a Way to Enhance Parents’ Communication in Cases of Oncological Pediatric Diseases

Cited by 3 publications

References 7 publications

Artificial Intelligence in Healthcare: 2023 Year in Review

Artificial Intelligence in Healthcare: 2023 Year in Review

Can Artificial Intelligence Assist Nurses in Planning the Nursing Care of a Child with Acute Lymphoblastic Leukemia?

Letter to the editor: Evaluating ChatGPT as a tool for educating caregivers on malaria vaccination for children

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