Comparing artificial intelligence to humans for endoscopic diagnosis of gastric neoplasia: An external validation study

Developing an Effective Off-the-job Training Model and an Automated Evaluation System for Thoracoscopic Esophageal Atresia Surgery

BACKGROUND

Pediatric minimally invasive surgery requires advanced technical skills. Off-the-job training (OJT), especially when using disease-specific models, is an effective method of acquiring surgical skills. To achieve effective OJT, it is necessary to provide objective and appropriate skill assessment feedback to trainees. We aimed to construct a system that automatically evaluates surgical skills based on forceps movement using deep learning (DL).

METHODS

Using our original esophageal atresia OJT model, participants were tasked with performing esophageal anastomosis. All tasks were recorded for image analysis. Based on manual objective skill assessments, each participant's surgical skills were categorized into two groups: good and poor. The motion of the forceps in both groups was used as training data. Employing this training data, we constructed an automated system that recognized the movement of forceps and determined the quality of the surgical technique.

RESULTS

Thirteen participants were assigned to the good skill group and 32 to the poor skill group. These cases were validated using an automated skill assessment system. This system showed a precision of 75%, a specificity of 94%, and an area under the receiver operating characteristic curve of 0.81.

CONCLUSIONS

We constructed a system that automatically evaluated the quality of surgical techniques based on the movement of forceps using DL. Artificial intelligence diagnostics further revealed the procedures important for suture manipulation.

LEVELS OF EVIDENCE

Level IV.

Artificial intelligence for gastric cancer in endoscopy: From diagnostic reasoning to market

Recognition of gastric conditions during endoscopy exams, including gastric cancer, usually requires specialized training and a long learning curve. Besides that, the interobserver variability is frequently high due to the different morphological characteristics of the lesions and grades of mucosal inflammation. In this sense, artificial intelligence tools based on deep learning models have been developed to support physicians to detect, classify, and predict gastric lesions more efficiently. Even though a growing number of studies exists in the literature, there are multiple challenges to bring a model to practice in this field, such as the need for more robust validation studies and regulatory hurdles. Therefore, the aim of this review is to provide a comprehensive assessment of the current use of artificial intelligence applied to endoscopic imaging to evaluate gastric precancerous and cancerous lesions and the barriers to widespread implementation of this technology in clinical routine.

AI and professional liability assessment in healthcare. A revolution in legal medicine?

The adoption of advanced artificial intelligence (AI) systems in healthcare is transforming the healthcare-delivery landscape. Artificial intelligence may enhance patient safety and improve healthcare outcomes, but it presents notable ethical and legal dilemmas. Moreover, as AI streamlines the analysis of the multitude of factors relevant to malpractice claims, including informed consent, adherence to standards of care, and causation, the evaluation of professional liability might also benefit from its use. Beginning with an analysis of the basic steps in assessing professional liability, this article examines the potential new medical-legal issues that an expert witness may encounter when analyzing malpractice cases and the potential integration of AI in this context. These changes related to the use of integrated AI, will necessitate efforts on the part of judges, experts, and clinicians, and may require new legislative regulations. A new expert witness will be likely necessary in the evaluation of professional liability cases. On the one hand, artificial intelligence will support the expert witness; however, on the other hand, it will introduce specific elements into the activities of healthcare workers. These elements will necessitate an expert witness with a specialized cultural background. Examining the steps of professional liability assessment indicates that the likely path for AI in legal medicine involves its role as a collaborative and integrated tool. The combination of AI with human judgment in these assessments can enhance comprehensiveness and fairness. However, it is imperative to adopt a cautious and balanced approach to prevent complete automation in this field.