Introducing B21 Scholar: Aimee Castro

Aimee Castro

Aimee Castro

Exploring the Intersection of Nursing and Artificial Intelligence

Who: Aimee Castro, MA, MSc(A), Doctoral Student

I am a graduate student in nursing who is inspired by the development of technologies that support caregiving. I recently began doctoral studies in nursing science in September 2018. Nursing is a broad, diverse, and beautiful discipline, so my vision and description of nursing in this blog may be different from other nurses’ descriptions of this discipline.

Purpose: At Building 21, I want to explore the question, “What can nursing scientists learn from AI researchers, and vice versa?

Rationale: Nurses have a broad knowledge base in medicine, health systems, interdisciplinary collaboration, psychology, ethics, and management. Nurses know how to help patients and families navigate health systems and achieve complex health- and care-related goals. Nurses’ biopsychosocial training can and should be used to humanize technology, so that technologies are built which truly support nurses and patients.

While nurses understand complex health and care concerns, they have very limited representation in technology development and big data research. Understanding the basic philosophy and methods behind AI may teach nurses how this way of thinking and researching can be applied towards solving nursing concerns.

Without nurses participating in this research, there are no guarantees that nursing interests will be used to inform the development of technologies that will be coming to health and care systems in the near future.

Plan: For this fellowship, I plan to immerse myself in the available academic and grey literature of AI. I will view all of this material through the lens of nursing, keeping track of potential connections where AI might benefit from a nursing perspective, as well as areas of nursing that might benefit from an AI approach.

Two publications will hopefully result: one will be a general overview of AI and nursing. The other will attempt a more in-depth analysis of the potential of speech recognition in nursing

Further Background



It’s easy to forget that neural networks are based on esoteric research from the 80s and 90s. However, in recent years the field has exploded. Whether it’s called “machine learning,” or “big data,” or “artificial intelligence,” researchers – particularly those in computer engineering – are learning how to use massive amounts of data to train computer algorithms to predict outcomes. AI tools are currently exploding into other fields, and are being used to predict and plan for driverless cars [i], epidemics [ii], and academic outcomes [iii].

To date, AI has had limited direct effect on nursing interests. In fact, some researchers argue that nursing and caregiving will be the last disciplines to be automated using AI. Yet, AI will eventually be built into products that affect patients and tools that nurses will be expected to use.

History shows that true innovation occurs at the boundaries of disciplines, but so far, nursing and AI researchers have siloed themselves [iv]. I want to play a part in finally bringing the best of nursing and AI together. In order to do so, I need time to study AI research through the lens of nursing.


[i] Attia, R., Orjuela, R., & Basset, M. (2014). Combined longitudinal and lateral control for automated vehicle guidance. Vehicle System Dynamics, 52(2), 261-279.

[ii] Fernandez-Luque, L., & Imran, M. (2018). Humanitarian Health Computing using Artificial Intelligence and Social Media: A Narrative Literature Review. International journal of medical informatics.


[iii] Umair, S., & Sharif, M. M. (2018). Predicting Students Grades Using Artificial Neural Networks and Support Vector Machine. In Encyclopedia of Information Science and Technology, Fourth Edition (pp. 5169-5182). IGI Global.

[iv] Risling, T. (2017). “Why AI Needs Nursing.” From

David Jhave Johnston