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This study aimed to evaluate the ability of three generative artificial intelligence tools (ChatGPT, Gemini and DeepSeek) to generate clinically accurate, comprehensive, and readable nursing care plans aligned with standardised nursing taxonomies (North American Nursing Diagnosis Association International, Nursing Interventions Classification, and Nursing Outcomes Classification). The study further explored variations in tool performance across different nursing specialties.
A descriptive comparative design was used.
Ten expert-validated clinical scenarios representing five nursing specialties (Fundamentals of Nursing, Medical, Surgical, Paediatric and Psychiatric Nursing) were presented to the three artificial intelligence tools. Each tool responded to four standardised prompts based on the latest North American Nursing Diagnosis Association International, Nursing Interventions Classification and Nursing Outcomes Classification taxonomies. Outputs were assessed for quality, accuracy, completeness and readability by expert evaluators using validated scales.
All tools produced nursing care plans of moderate-to-high quality. DeepSeek demonstrated slightly higher accuracy and completeness compared with Gemini and ChatGPT. Surgical nursing scenarios yielded the highest performance, likely reflecting the more protocolised and pathway-driven nature of perioperative care. However, all outputs were incomplete and written at a college-level readability, limiting accessibility for clinical use.
Generative artificial intelligence tools can support the production of structured nursing care plans requiring expert review and adaptation, particularly in less standardised clinical domains, but their limitations in completeness and readability indicate they should be regarded only as preliminary drafts requiring expert review and adaptation.
The study examined whether generative artificial intelligence can reliably assist in creating nursing care plans. All tools performed moderately well, with DeepSeek showing slight advantages, but outputs were incomplete and difficult to read. Findings are relevant to clinical nurses, educators, healthcare managers and policymakers worldwide who are exploring artificial intelligence in nursing workflows.
This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.
This study did not include patient or public involvement in its design, conduct or reporting.
Despite the benefits of early diagnosis, most cancers in sub-Saharan African (SSA) countries are diagnosed at an advanced stage due to late presentation of symptoms, inadequate referral systems and poor diagnostic capacity. Health communication interventions have been used extensively in high-income countries to increase people’s awareness of cancer symptoms and encourage timely help-seeking. However, in SSA, there is still limited evidence on the effectiveness of these interventions and existing evaluations are mainly focused on communicable diseases rather than cancer.
A randomised, multisite, controlled community trial will evaluate a culturally tailored health infographic toolkit delivered in rural and urban settings in the Western Cape Province in South Africa and Harare and surrounding provinces in Zimbabwe. Participants will be randomised to receive one of three African aWAreness of CANcer and Early Diagnosis (AWACAN-ED) cancer awareness tools, coproduced with local communities, comprising health communication infographics with descriptions of breast, cervical and colorectal cancer symptoms plus messages to encourage consultation with primary care providers if symptoms occur, all presented in English and four local languages. We will recruit 144 participants in each of the three intervention groups (N=432). The primary outcome will be recall of symptoms and the secondary outcomes will be (1) intention to seek help, (2) emotional impact and (3) acceptability of the toolkit. Outcomes will be measured preintervention and at two points postintervention: after 15 min and 1 month.
Ethical approval was obtained in both participating countries, South Africa (148/2025) and Zimbabwe (363/2021). All participants will be required to provide written informed consent prior to participation. Findings will be disseminated through peer-reviewed publications, conference presentations and the AWACAN-ED programme website.
PACTR202505475803308.
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