Ethical implications of AI in robotic surgical training: A Delphi consensus statement

CONTEXT

As the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them.

OBJECTIVES

To provide ethical guidance on developing narrow AI applications for surgical training curricula. We define standardised approaches to developing AI driven applications in surgical training that address current recognised ethical implications of utilising AI on surgical data. We aim to describe an ethical approach based on the current evidence, understanding of AI and available technologies, by seeking consensus from an expert committee.

EVIDENCE ACQUISITION

The project was carried out in 3 phases: (1) A steering group was formed to review the literature and summarize current evidence. (2) A larger expert panel convened and discussed the ethical implications of AI application based on the current evidence. A survey was created, with input from panel members. (3) Thirdly, panel-based consensus findings were determined using an online Delphi process to formulate guidance. 30 experts in AI implementation and/or training including clinicians, academics and industry contributed. The Delphi process underwent 3 rounds. Additions to the second and third-round surveys were formulated based on the answers and comments from previous rounds. Consensus opinion was defined as ≥ 80% agreement.

EVIDENCE SYNTHESIS

There was 100% response from all 3 rounds. The resulting formulated guidance showed good internal consistency, with a Cronbach alpha of >0.8. There was 100% consensus that there is currently a lack of guidance on the utilisation of AI in the setting of robotic surgical training. Consensus was reached in multiple areas, including: 1. Data protection and privacy; 2. Reproducibility and transparency; 3. Predictive analytics; 4. Inherent biases; 5. Areas of training most likely to benefit from AI.

CONCLUSIONS

Using the Delphi methodology, we achieved international consensus among experts to develop and reach content validation for guidance on ethical implications of AI in surgical training. Providing an ethical foundation for launching narrow AI applications in surgical training. This guidance will require further validation.

PATIENT SUMMARY

As the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them.In this paper we provide guidance on ethical implications of AI in surgical training.

Comparison between the classical and a modified trans-septal technique of alar cinching for Le Fort I osteotomies: a prospective randomized controlled trial

The aim of this prospective randomized control trial was to analyse the efficacy of a new trans-septal alar base cinch suture in controlling alar width in patients undergoing maxillary intrusion and setback by comparing it with the traditional cinch suture. Statistical evaluation was carried out in 62 of 76 patients. Group I (31 patients) received the traditional alar base cinch suture, and group II (31 patients) received the alar base cinch suture with an anchoring bite taken through the nasal septum 10mm behind its anterior edge. In both groups the accurate identification of alar fibroareolar tissue was facilitated by an 18 gauge green needle passed extra orally. Alar base width was measured before and 6 months after surgery using Vernier callipers. Preoperative alar base width for group I was 29.76 mm (1.901SD) and for group II 29.79 mm (3.141SD); the postoperative values were 32.42 mm (1.858SD) and 29.94 mm (2.568SD), respectively. Mean alar base widening was 2.661 mm (0.800SD) in group I and 0.145 mm (2.050) in group II. The difference in alar widening was statistically significant (p<0.001). In conclusion, the trans-septal modified alar cinch suture offers better control of alar base architecture in maxillary intrusion and setback.