When Intuition Meets the Algorithm: Medico-Legal Implications of Artificial Intelligence-Driven Decision-Making in Orthopedics

Orthopedic surgery is undergoing a transformation driven by artificial intelligence (AI), which is reshaping clinico-surgical decision-making. While the operative strategy and professional responsibility traditionally relied on the surgeon's intuition and manual skills, advanced algorithms now provide predictive, analytical, and procedural decision supports. This paradigm shift is redefining the concept of human error as well as the relationship between technological tools and human decision-makers. As a result, the foundational elements of the healthcare liability framework are being affected. This paper offers a narrative discussion on selected applications of artificial intelligence in orthopedic surgical practice, including patient risk stratification, surgical indication and prosthesis positioning, with a particular focus on the liability implications for healthcare professionals who rely on these systems in terms of therapeutic decision-making. The aim is then to provide a comprehensive medico-legal perspective within the highly regulated and high-risk field of biomedicine, acknowledging and critically assessing the roles and responsibilities of all stakeholders involved-patients, healthcare professionals, innovative technologies, healthcare organizations, and facility management-while balancing innovation, evidence-based practice, and accountability in healthcare delivery.

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Publication Details

Journal
Bioengineering
Published
2026-02-15
DOI
https://doi.org/10.3390/bioengineering13020227
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

When Intuition Meets the Algorithm: Medico-Legal Implications of Artificial Intelligence-Driven Decision-Making in Orthopedics

L Tronconi, Giulia Bambagiotti, Luca Bianco Prevot, Vittorio Bolcato et al.
Bioengineering
Artificial Intelligence in Healthcare and Education
article

When Intuition Meets the Algorithm: Medico-Legal Implications of Artificial Intelligence-Driven Decision-Making in Orthopedics

L Tronconi, Giulia Bambagiotti, Luca Bianco Prevot, Vittorio Bolcato, Giuseppe Basile
article en

Abstract

Orthopedic surgery is undergoing a transformation driven by artificial intelligence (AI), which is reshaping clinico-surgical decision-making. While the operative strategy and professional responsibility traditionally relied on the surgeon's intuition and manual skills, advanced algorithms now provide predictive, analytical, and procedural decision supports. This paradigm shift is redefining the concept of human error as well as the relationship between technological tools and human decision-makers. As a result, the foundational elements of the healthcare liability framework are being affected. This paper offers a narrative discussion on selected applications of artificial intelligence in orthopedic surgical practice, including patient risk stratification, surgical indication and prosthesis positioning, with a particular focus on the liability implications for healthcare professionals who rely on these systems in terms of therapeutic decision-making. The aim is then to provide a comprehensive medico-legal perspective within the highly regulated and high-risk field of biomedicine, acknowledging and critically assessing the roles and responsibilities of all stakeholders involved-patients, healthcare professionals, innovative technologies, healthcare organizations, and facility management-while balancing innovation, evidence-based practice, and accountability in healthcare delivery.

BioengineeringVol. 13(2)
Marche Polytechnic University (IT), Santa Maria Nuova Hospital (IT), European University of Rome (IT), Maria Cecilia Hospital (IT), IRCCS Ospedale Galeazzi - Sant'Ambrogio (IT)
Openalex Percentile: Top 56%
Artificial Intelligence in Healthcare and Education
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When Intuition Meets the Algorithm: Medico-Legal Implications of Artificial Intelligence-Driven Decision-Making in Orthopedics — L Tronconi, Giulia Bambagiotti, et al. · Bioengineering (2026) | TGRS Research Map | TGRS