**5.2 AI and shared decision making**

Shared decision making (SDM) is an approach where the patient and provider work in concert to formulate evidence-based medical choices that align with patient values [78]. Thus, the final choice is based on what matters most to the patient with medical data as an adjunct [79]. Unfortunately, real world applicability is lacking [80–82].

Constraints on time, difficulties with generalizability, and medical circumstances are all obstacles to SDM [83]. AI may advance SDM through better informed decision-making, which allows providers to concentrate their energy on the patient [84]. Furthermore, AI may discover missed correlations by individuals participating in clinical assessments [85]. Nevertheless, bioethical concerns for AI and health decision-making remain [84]. Moreover, AI-based decision aids remain foreign in regards to their patient-centeredness [86]. Lastly, the facilitation of AI and shared decision making remains unknown. A three step scheme has been offered [87, 88]. It is further depicted in (**Figure 4**).

A scoping review showed the range of AI systems applied to SDM [89]. Sadly, few studies concerned primary care. Of the involved studies, three devised AI interventions for primary care involving the support of chronic conditions such as diabetes and stroke [90–92]. These studies focused on the decision-making step of SDM either by launching trials to calculate clinically significant results or for medical advice. Wang et al. aimed to tailor knowledge and choices about medications in type 2 diabetics [92]. SDM is essential secondary to the complexity of diabetics. In this report information from an EHR was compiled to aid clinicians with decision support tools to enable patients to better comprehend their well-being. Over 2500 patients with type 2 diabetes, 77 features, and eight different medications were amassed to generate a prototype for reference. The AI model had a correctness of 0.76. The records just pertained to hospitalized individuals and the result of medication utilization was not accounted for. Still, the intervention exhibited practicability and adaptability, meaning if the scheme did not remain current, the mediation could be fine-tuned without any impact to the interoperability of the hospital EHR. Moreover, the program was created with the patient in mind, which allowed key stakeholders to evaluate an individual's ailment more systematically and modify discussions in an up-to-date manner.

Kökciyan et al. made "CONSULT," a decision-support agent to help stroke survivors in treatment compliance and self-care in partnership with a practioner [90, 91]. It was generated through an argumentation construct, which is beyond the scope of this chapter. However, a brief description is as follows. Health sensors and EHR information as well as medical standards were used as inputs. Proposals and written descriptions for systematized choices were provided as outputs. The program was carried out with a mobile Android app. Six unpaid workers in decent health were


*3-step model of shared decision making (SDM) for clinical practice.*
