**9. AI: hope or hype? methodological, ethical, medicolegal issues, and patient perspectives**

AI is a relatively novel clinical tool, as such the healthcare provider should be cautious before adopting its methods and incorporating them into clinical routine. The following limitations are uniform across medicine, not BMS alone, and prompt the implementation of a solid frame in the context of which AI may yield its most beneficial aspects in clinical practice. Extensive analysis of AI limitations is beyond the scope of this chapter; therefore, we will attempt to outline the most important aspects of them.

One of the advantages of AI is the management of large amounts of data, but at the same time this is a prerequisite to obtain reliable outcomes. As such, data quantity is one issue. Data quality is another, and this can be achieved only when source data (i.e., registries or electronic health records—EHRs) are comprehensive and inclusive. In other words, all patients should have access to health services irrespective of socioeconomic status, and health services on their part should promote continuity of care instead of segmentation. The third important component is model interpretation, especially when it comes to deep learning, given that sometimes the relations between inputs (predictors) and outcomes are not obvious. Next, model generalizability and interoperability are paramount for implementing AI algorithms across different health systems and contexts, and these can be ensured only when the three former methodological requirements are met. Finally, AI researchers must ensure model security, i.e., avoid "contamination" of data. This is a potential issue even after meticulous training of data [105]. To address these potential sources of bias, several strategies have been proposed. Among them, oversampling minority groups in training datasets, creating flags for certain high-risk groups, and formulating baseline predictions at presentation of illness (i.e., in the case of BMS, before surgery) are the most feasible ones [106].

The usage of AI as a decision-making tool may also have medicolegal sequelae. In this case, one should take into consideration all the parameters, i.e., agreement between AI recommendation and standard of care, accuracy of AI prediction, physician action (acceptance or rejection of the AI decision), and patient outcome. Different combinations may lead to different legal outcomes, i.e., no injury of the patient and no liability of the surgeon, injury but no liability, or both injury and liability [107]. Consequently, on the one hand, healthcare providers should know how to interpret AI algorithm outcomes and recruit their clinical judgment above all; on the other hand, they should have an active role in shaping their liability issue through their professional societies and legislation-forming organizations.

Is the role of the surgeon threatened by the advent of AI? Are surgeons transforming from leaders to simple operators of what a machine has decided for a patient? Definitely not. AI should be deemed as a tool that is intended to assist surgeons in their daily workflow and ease their work with the intent to help them focus on what is important, i.e., physician-patient relationship. Additionally, AI offers a real opportunity for individualized interventions and precision medicine, not only at the time of operations, must (even most importantly) during the postoperative period and follow-up.

*Current and Potential Applications of Artificial Intelligence in Metabolic Bariatric Surgery DOI: http://dx.doi.org/10.5772/intechopen.106365*

What impact does AI make on patients themselves? According to a recently published survey, it depends on the context. Fifty-five percent of participants were very or somewhat comfortable with AI making chest X-ray diagnosis, but the respective percentage for making cancer diagnosis dropped to 31.2% [108]. Consequently, the role of the surgeon remains central to continuum of healthcare provision, while discussing all diagnostic and therapeutic options with the patient is indispensable.

As it has been stressed out by Bellini et al., AI has contributed to substantial progress in decision-making, quality of care, and precision medicine, but several legal and ethical issues need to be addressed before its widespread application in clinical practice [109].

### **10. Conclusions**

AI is gaining more and more ground to clinical practice, as it has been documented not only by our research [4], but also that of other investigators within the same context [109]. The clinician is not required to understand how AI algorithms work but should be cautious when interpreting AI-based outcomes and decision by evaluating its source data and metrics. For reasons of simplicity, AI should be considered a novel statistical tool with the advantage of yielding data from large, real-world registries of patients rather than restricted cohorts as the ones used in the context of randomized trials. Given the specialized nature of processing these data, specialists such as data scientists could assume new roles in the multidisciplinary team of managing bariatric patients.

### **Conflict of interest**

The author declares no conflict of interest.
