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Abstract Background Cotard’s Syndrome (CS) is a rare clinical entity where patients can report nihilistic, delusional beliefs that they are already dead. Curiously, while weight loss, dehydration, and metabolic derangements have been described as discussed above, a review of the literature revealed neither a single case of a severely underweight patient nor a serious metabolic complication such as Diabetic Ketoacidosis. Further, a search on PubMed revealed no articles discussing the co-occurrence of Cotard’s Delusion and eating disorders or comorbid metabolic illnesses such as diabetes mellitus. In order to better examine the association between Cotard’s Delusion and comorbid eating disorders and metabolic illness, we will present and discuss a case where Cotard’s delusion led to a severe metabolic outcome of DKA and a BMI of 15. Case presentation Mr. B is a 19 year old transgender man admitted to the hospital due to diabetic ketoacidosis secondary to Type 1 Diabetes Mellitus. Mr. B had a history of Obsessive–Compulsive Disorder, Major Depressive Disorder, and Post-Traumatic Stress Disorder. The primary pediatric team discovered that Mr. B had not been using his insulin appropriately and was severely underweight, and they believed this could be due to his underlying mental illness. The psychiatric consultation/liaison service found that Mr. B was suffering from Cotard’s delusion leading him to be noncompliant with his insulin due to a belief that he was already dead. Cotard’s delusion had in this case led to a severe metabolic outcome of DKA and a BMI of 15. Conclusions This case provides clinical insight into the interactions of eating disorders and Cotard’s delusion as well as the potential medical complications when Cotard’s delusion is co-morbid with medical conditions such as Diabetes Mellitus. We recommend that clinicians routinely screen patients for Cotard’s delusion and assess whether the presence of which could exacerbate any underlying medical illness. This includes clinicians taking special care in assessing patient’s caloric and fluid intake as well as their adherence to medications both psychiatric and medical. Further research could be conducted to explore the potential overlap of Cotard’s delusion and eating disorder phenomenology.

Artificial intelligence (AI) has the potential to improve diagnostic accuracy. Yet people are often reluctant to trust automated systems, and some patient populations may be particularly distrusting. We sought to determine how diverse patient populations feel about the use of AI diagnostic tools, and whether framing and informing the choice affects uptake. To construct and pretest our materials, we conducted structured interviews with a diverse set of actual patients. We then conducted a pre-registered (osf.io/9y26x), randomized, blinded survey experiment in factorial design. A survey firm provided n = 2675 responses, oversampling minoritized populations. Clinical vignettes were randomly manipulated in eight variables with two levels each: disease severity (leukemia versus sleep apnea), whether AI is proven more accurate than human specialists, whether the AI clinic is personalized to the patient through listening and/or tailoring, whether the AI clinic avoids racial and/or financial biases, whether the Primary Care Physician (PCP) promises to explain and incorporate the advice, and whether the PCP nudges the patient towards AI as the established, recommended, and easy choice. Our main outcome measure was selection of AI clinic or human physician specialist clinic (binary, "AI uptake"). We found that with weighting representative to the U.S. population, respondents were almost evenly split (52.9% chose human doctor and 47.1% chose AI clinic). In unweighted experimental contrasts of respondents who met pre-registered criteria for engagement, a PCP's explanation that AI has proven superior accuracy increased uptake (OR = 1.48, CI 1.24-1.77, p < .001), as did a PCP's nudge towards AI as the established choice (OR = 1.25, CI: 1.05-1.50, p = .013), as did reassurance that the AI clinic had trained counselors to listen to the patient's unique perspectives (OR = 1.27, CI: 1.07-1.52, p = .008). Disease severity (leukemia versus sleep apnea) and other manipulations did not affect AI uptake significantly. Compared to White respondents, Black respondents selected AI less often (OR = .73, CI: .55-.96, p = .023) and Native Americans selected it more often (OR: 1.37, CI: 1.01-1.87, p = .041). Older respondents were less likely to choose AI (OR: .99, CI: .987-.999, p = .03), as were those who identified as politically conservative (OR: .65, CI: .52-.81, p < .001) or viewed religion as important (OR: .64, CI: .52-.77, p < .001). For each unit increase in education, the odds are 1.10 greater for selecting an AI provider (OR: 1.10, CI: 1.03-1.18, p = .004). While many patients appear resistant to the use of AI, accuracy information, nudges and a listening patient experience may help increase acceptance. To ensure that the benefits of AI are secured in clinical practice, future research on best methods of physician incorporation and patient decision making is required.