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Page 2 of 9 Peek et al. Mini-invasive Surg 2024;8:11 https://dx.doi.org/10.20517/2574-1225.2024.47

adenocarcinoma in the Western world further propelled the adoption of the transhiatal approach.
Nevertheless, traditional anastomosis posed challenges due to the delicate nature of deep viscera,
highlighting the significance of mechanical staplers as a pivotal innovation in the surgical subspecialty,
[6]
reducing both the incidence and severity of anastomotic fistulas . The advent of laparoscopic techniques
provided a less invasive alternative to traditional open approaches for esophageal carcinoma, offering
advantages such as decreased postoperative pain and shorter hospital stays. Jim Leketich’s development and
widespread adoption of totally minimally invasive esophagectomy in 1993 marked a significant milestone in
[7]
surgical treatment . Following the approval of the Da Vinci robotic surgical system by the Food and Drug
Administration (FDA) in 2000, Broussard et al. pioneered robotic esophagectomy in 2002 . Since then,
[8]
robotic technology has gained traction in esophageal surgery, with steady increases in utilization and
efficacy.

ESOPHAGECTOMY TODAY AND TOMORROW
The Da Vinci robotic platform, approved by the FDA and developed by Intuitive Inc.®, serves as a valuable
tool in various abdominal procedures, particularly those involving intricate and confined anatomical
regions. Core features of this system include three primary components: articulated arms with free mobility,
advanced three-dimensional high-definition video imaging capabilities, and a human-computer interaction
design integrated into the primary control console . While introducing robotic assistance has not notably
[9]
altered the indications for esophagectomy or the specific surgical methodologies employed, its integration
enables surgeons to expand the pool of eligible patients for procedures. This technology provides superior
visualization compared to two-dimensional laparoscopic approaches and effectively mitigates tremors,
enhancing surgical precision. Other notable advantages include decreased blood loss, fewer incisions, and
an expedited recovery period [10,11] . Furthermore, studies indicate that, compared to non-robotic approaches,
robotic operations do not exhibit significant disparities in terms of charges, costs, or profitability. However,
the primary obstacle to widespread adoption of robotic technology in surgery remains its high financial
investment. Consequently, refining operative strategies and optimizing outcomes in esophageal resections
becomes imperative for effectively mastering robotic esophagectomy . In recent developments, our team
[12]
has concluded training sessions for the Single Port Da Vinci Robot. As part of this progression, we are
poised to initiate the inaugural surgeon-led investigation device exemption (IDE) pilot study for Single Port
Da Vinci Foregut and hepatopancreaticobiliary (HPB) operations. This milestone marks a significant
advancement for the robotic platform, highlighting the efficacy of this cutting-edge technology in
enhancing patient outcomes.

Ongoing advancements in robotic technology, imagining modalities, and surgical techniques continue to
refine the field of esophageal surgery, focusing on improving outcomes, reducing morbidity, and enhancing
patient experience. The trajectory of robotic assisted minimally invasive esophagectomy (RAMIE) is
intricately intertwined with advancements in robotic platforms. Anticipated in the coming years is the
emergence of multiple novel hardware systems. Recent enhancements in robotic tri-stapling devices, energy
dissection instruments, and Firefly integration have streamlined the procedure. The most significant strides
in robotic surgery are expected to stem from software innovations. Incorporating artificial intelligence (AI),
data integration, and image connectivity will not only enhance precision surgery but also facilitate extensive
data collection and machine learning capabilities [13-16] . For a succinct overview, the integration of AI in
surgery encompasses machine learning (ML) and deep learning (DL) methodologies designed to emulate
the cognitive processes of the human brain, thereby enhancing comprehension of intricate scenarios and
facilitating improved decision-making capabilities. These algorithms, ML and DL, are rapidly advancing the
prospects of autonomous surgical interventions . By applying AI in robotic surgery, surgeons can harness
[17]
the potential of these emerging technologies to enhance various aspects of the surgical process, spanning