Reported by Simon Daniel Yusuph l Journalist at Weng Global
Healthcare experts and nursing organisations are calling for the responsible adoption of artificial intelligence (AI) in nursing, arguing that properly governed technology could help nurses identify risks earlier, improve clinical workflows and strengthen patient safety without replacing professional judgment.
The growing discussion comes as AI becomes increasingly integrated into healthcare, including clinical decision support, documentation, patient education, research and other nursing-related activities.
Nursing leaders, however, say the expansion of AI must be accompanied by safeguards that protect patients, preserve nurses’ clinical independence and ensure that technology supports rather than replaces human decision-making.
The American Nurses Association (ANA), following an AI in Nursing Practice Think Tank held in April 2026, identified patient safety, professional judgment, accountability, algorithmic bias and the potential for increased cognitive burden among the major issues that need to be addressed as AI expands in nursing.
AI could help nurses identify risks earlier
One of the central arguments for greater use of AI in nursing is its ability to process large amounts of clinical information and highlight patterns that may require attention.
Nurses routinely work with information from patient observations, medical records, laboratory results, medication records and other clinical systems. AI-enabled tools can potentially help organise that information and provide decision-support signals that nurses can assess alongside their own clinical knowledge.
The objective is not to transfer responsibility for patient care to a machine.
Instead, supporters of responsible AI adoption argue that technology should provide additional information that allows nurses to spend more time on assessment, communication and direct patient care.
The American Academy of Nursing said in its 2026 position statement on AI in healthcare that artificial intelligence should support, rather than replace, human wisdom and nursing clinical judgment.
The Academy also called for human oversight, stronger AI literacy, transparency, privacy protections and involvement of nurses in the development and evaluation of AI systems.
Patient safety remains the central concern
The potential benefits of AI are accompanied by significant risks, particularly when systems are introduced into clinical environments without sufficient testing or oversight.
A 2026 survey of senior nursing leaders published by Elsevier found that patient safety was among the leading concerns surrounding AI adoption in nursing. The survey indicated that many healthcare organisations remain in pilot stages rather than having fully integrated AI across nursing workflows.
This cautious approach reflects the consequences of errors in healthcare.
An inaccurate AI-generated recommendation, incomplete patient information or biased algorithm could affect clinical decisions if nurses or other healthcare professionals accept the technology’s output without appropriate verification.
For that reason, nursing organisations have increasingly stressed that AI should function as a support tool rather than an independent authority over patient care.
The ANA has specifically called for nurse-led guardrails and greater involvement of nurses in determining how AI technologies are designed, implemented and evaluated.
Nurses must remain part of the decision-making process
The growing role of nurses in AI governance is significant because nurses interact with patients continuously and often have information that may not be fully captured by digital systems.
A nurse may observe changes in a patient’s behaviour, appearance, responsiveness or condition that are difficult to reduce to a single data point.
That makes professional judgment particularly important when technology produces recommendations or alerts.
The American Academy of Nursing has therefore recommended human-in-the-loop oversight for AI governance and stressed the importance of including nurse scientists and nurse informaticists in technology development, procurement, implementation and evaluation.
Such participation can help ensure that AI systems are designed around actual clinical workflows rather than assumptions about how nursing care is delivered.
It can also help identify problems before technologies become deeply embedded in hospital systems.
AI adoption among nurses remains uneven
Despite growing interest in artificial intelligence, adoption among nurses is not yet universal.
Elsevier’s Clinician of the Future 2026: Nurses Edition, based on a global survey of 2,757 clinicians across 118 countries, found that 41 per cent of nurses surveyed used AI for work, compared with 57 per cent of doctors.
The report also found that nurses were using general-purpose AI for activities such as professional education, patient education and medical research, while the use of clinical-specific AI tools remained more limited.
The findings suggest that the issue is not simply whether nurses are willing to use AI.
Healthcare organisations also need to determine whether the tools available to nurses are specifically designed for nursing responsibilities and whether they can be trusted in clinical settings.
Elsevier reported that only 42 per cent of nurses surveyed considered AI tools trustworthy at the time of the study, while 61 per cent believed clinicians using AI would provide better care over the following five to 10 years.
That gap between future optimism and present trust highlights the importance of evidence, transparency and responsible implementation.
AI should complement, not replace, nursing judgment
The principle of human oversight is becoming a recurring theme in discussions about AI and nursing.
The American Nurses Association states that nurses must remain accountable decision-makers for patient care, with AI serving as a source of information rather than a replacement for clinical reasoning, ethical responsibility and human connection.
This distinction is important because nursing involves more than interpreting clinical data.
Nurses also communicate with patients and families, assess changing circumstances, coordinate care and make judgments within complex social and clinical environments.
AI can process information rapidly, but the final interpretation of that information still requires appropriately trained healthcare professionals who understand the individual patient and the circumstances surrounding their care.
The American Academy of Nursing similarly argues that AI should be developed around person-centred care while protecting professional autonomy and the nurse-patient relationship.
Training will become increasingly important
As AI becomes more common in hospitals and other healthcare settings, nurses will require sufficient knowledge to understand how these systems work and, importantly, when their outputs should be questioned.
AI literacy can include understanding how to verify AI-generated information, recognise potential errors or bias, protect patient information and understand the limitations of automated systems.
A 2026 study published in Nursing Outlook argued that AI literacy should become part of nursing education and that rigorous implementation approaches are needed to ensure AI is introduced effectively.
The researchers also highlighted the importance of partnerships between nurse scientists, technology developers and other disciplines.
Such collaboration can help ensure that the development of AI is informed by clinical realities rather than driven solely by technological capabilities.
Privacy and accountability remain major challenges
Patient data is another critical issue.
AI systems depend on data, and healthcare information can include highly sensitive personal and medical details. Organisations adopting these technologies therefore need strong safeguards around data access, storage, security and use.
Questions of accountability also remain important.
If an AI system generates an incorrect recommendation that contributes to a poor outcome, healthcare organisations need clear rules establishing who is responsible for reviewing and acting on that information.
The American Nurses Association has identified unclear accountability and liability as among the risks associated with AI in healthcare.
The American Academy of Nursing has similarly called for policies that protect privacy, strengthen transparency and ensure that healthcare professionals remain accountable for patient care.
Technology must fit the realities of nursing
Another important consideration is whether AI actually improves nursing workflows.
Technology that generates additional alerts, requires complicated processes or produces information that nurses cannot easily interpret could increase rather than reduce pressure on healthcare workers.
Research and industry surveys increasingly suggest that nurses want AI tools that are practical, transparent, safe and grounded in reliable information.
McKinsey’s 2026 Nursing AI Insights Survey, for example, found that nurses identified data security and privacy, evidence that AI improves quality and patient safety, clear regulations, transparency and training among important measures for increasing confidence in AI.
This indicates that successful adoption cannot be measured simply by the number of hospitals purchasing AI systems.
The more important question is whether those systems deliver useful support without creating new risks for patients or additional burdens for nurses.
Why responsible AI adoption matters
The debate over AI in nursing extends beyond technology.
It is ultimately a question of how healthcare systems can use new tools while maintaining patient trust and professional accountability.
For countries facing shortages of healthcare workers, growing patient numbers and pressure on existing health systems, AI could offer opportunities to support clinical teams.
But those opportunities will depend on the quality of implementation.
For African healthcare systems in particular, the adoption of AI may require consideration of infrastructure, connectivity, data quality, workforce training, affordability, language and local clinical realities.
Technology developed for one healthcare environment may not automatically perform effectively in another.
Healthcare institutions therefore need to evaluate AI tools against the needs of their own patients and healthcare workers rather than assuming that imported systems will produce identical results.
What happens next
The expansion of AI in nursing is likely to continue, but professional organisations are pushing for a model in which nurses remain central to decisions about how the technology is used.
The immediate priorities include stronger AI education, clinical validation, privacy protection, transparent governance, nurse participation in technology development and clear accountability for AI-assisted decisions.
The American Nurses Association has identified nurse-led guardrails, AI education, policy development and cross-sector collaboration among the actions needed as AI becomes more prominent in nursing.
For healthcare providers, the challenge will be balancing technological innovation with patient safety.
AI may help nurses process information and identify potential risks, but its effectiveness will depend on the quality of the underlying data, the reliability of the system and the ability of healthcare professionals to interpret its recommendations appropriately.
The emerging consensus among nursing organisations is therefore not that AI should replace nurses, but that it should be developed and deployed in ways that strengthen their ability to provide safe, effective and compassionate care.
As healthcare systems continue to experiment with artificial intelligence, the measure of successful adoption will ultimately be whether the technology improves care while preserving the human judgment and accountability at the heart of nursing.
Weng Global – stories beyond borders
Sources
- American Nurses Association — AI in Nursing Practice Think Tank and 2026 consensus findings.
- American Academy of Nursing — Position Statement on Artificial Intelligence in Health Care, March 2026.
- Elsevier — Clinician of the Future 2026: Nurses Edition.
- Elsevier — Global study on clinicians and AI adoption, May 2026.
- McKinsey — 2026 Nursing AI Insights Survey.
- Nursing Outlook — “Artificial intelligence and nursing science: Opportunities, challenges, implications, and guidelines.”
