AI technologies are rapidly advancing and have shown potential for providing significant value across a variety of sectors, including healthcare. Much of research has focused on the technologies’ capabilities and pushing their boundaries, with many envisioning AI and AI-enabled robots replacing human labor and humans in the near future. However, in critical domains of professional practice such as healthcare, full replacement is neither realistic nor aimed for, and collaboration between AI and humans is a given for the foreseeable future. This article argues for a shift away from a sole focus on the efficiency and effectiveness of technology, proposing instead that AI-enabled technologies increasingly should learn to adapt to human users considering that healthcare professionals already are overburdened. Rather than contributing to this burden, AI might extend the professional self by anticipating and supporting human needs and intentions. Drawing on a selective meta-synthesis of recent reviews and studies, this article introduces the concept of the AI-extended professional self. This concept suggests a temporary, dynamic integration of human professionals with AI that extends their capabilities with minimal additional burdens regarding training and application. Through three exemplars from healthcare—healthcare consultations, breast cancer screening, and robotic surgery—this article explores how a perspective rooted in the AI-extended professional self might unlock the potential for deeper AI integration into professional practice. Beyond these exemplars, this article calls for interdisciplinary research into the associated potential and challenges, advocating that the burden of AI integration needs to shift from humans to AI-enabled technologies.

The AI-extended professional self: user-centric AI integration into professional practice with exemplars from healthcare

Godono, Alessandro
Last
2025-01-01

Abstract

AI technologies are rapidly advancing and have shown potential for providing significant value across a variety of sectors, including healthcare. Much of research has focused on the technologies’ capabilities and pushing their boundaries, with many envisioning AI and AI-enabled robots replacing human labor and humans in the near future. However, in critical domains of professional practice such as healthcare, full replacement is neither realistic nor aimed for, and collaboration between AI and humans is a given for the foreseeable future. This article argues for a shift away from a sole focus on the efficiency and effectiveness of technology, proposing instead that AI-enabled technologies increasingly should learn to adapt to human users considering that healthcare professionals already are overburdened. Rather than contributing to this burden, AI might extend the professional self by anticipating and supporting human needs and intentions. Drawing on a selective meta-synthesis of recent reviews and studies, this article introduces the concept of the AI-extended professional self. This concept suggests a temporary, dynamic integration of human professionals with AI that extends their capabilities with minimal additional burdens regarding training and application. Through three exemplars from healthcare—healthcare consultations, breast cancer screening, and robotic surgery—this article explores how a perspective rooted in the AI-extended professional self might unlock the potential for deeper AI integration into professional practice. Beyond these exemplars, this article calls for interdisciplinary research into the associated potential and challenges, advocating that the burden of AI integration needs to shift from humans to AI-enabled technologies.
2025
1
12
Artificial intelligence; Extended self; Healthcare domain; Highlights; Professional practice
Schneider-Kamp, Anna; Godono, Alessandro
File in questo prodotto:
File Dimensione Formato  
The AI‑extended professional self.pdf

Accesso aperto

Tipo di file: PDF EDITORIALE
Dimensione 859.9 kB
Formato Adobe PDF
859.9 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/2072053
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact