Several epidemiological studies have investigated the link between silicone breast implants (SBIs) and autoimmune/rheumatic disorders, reporting inconsistent results. We aimed to evaluate the association between SBIs and the most clinically relevant autoimmune/rheumatic disorders using a large, population-based database. In this cross-sectional study, we used the computerized databases of Maccabi Healthcare Services (MHS), which include up to 20 years of data on 2 million members. Women with SBIs were identified by procedure and diagnosis codes, clinical breast examinations and mammography referrals. Autoimmune/rheumatic disorders were identified using the International Classification of Diseases 9th revision (ICD-9) codes. Multivariable logistic regression models were used to calculate odds ratios (ORs) and 95% confidence intervals (CIs). A Cox's proportional hazards model was used to calculate the hazard ratios (HRs) and 95% CIs among a subgroup of SBI recipients for whom the year of SBIs insertion was available. We included 24 651 SBI recipients and 98 604 matched SBI-free women. The adjusted OR between SBIs and being diagnosed with any autoimmune/rheumatic disorders was 1.22 (95% CI 1.18-1.26). The strongest association with SBIs (OR > 1.5, p < 0.001) was recorded for Sjögren's syndrome, systemic sclerosis (SSc) and sarcoidosis (OR of 1.58, 1.63 and 1.98, respectively). Similar results were calculated when analysis was limited to women with no breast cancer history. A multivariable Cox regression model yielded a HR of 1.45 (95% CI 1.21-1.73) for being diagnosed with at least one autoimmune/rheumatic disorder in women with SBI compared with those without. SBIs seem to be associated with higher likelihood of autoimmune/rheumatic disorders diagnosis.

Purpose of review: Fibromyalgia syndrome (FMS) is defined as chronic, widespread musculoskeletal pain and tenderness with concomitant mood and cognitive dysfunction. Several comorbidities have been reported to be associated with FMS. We reviewed the literature concerning the most noteworthy chronic conditions associated with FMS.Recent findings: There is mounting evidence displaying the concurrence of fibromyalgia and coexisting medical and psychiatric conditions. Such comorbidities may blur the classical clinical presentations and erroneously lead to misinterpretation of disease activity. The recognition of this fact should be underlined, as misrecognition may lead to excessive therapy and avoidable side-effects of medications on the one hand and to a better handling of FMS on the other hand, leading to improved clinical outcomes.Summary: A greater proportion of psychiatric and rheumatologic disorders are associated with FMS patients than the population. Consequently, physicians treating patients with either condition should keep in mind that these patients may have such comorbidities and should be treated accordingly.

Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in large language models pose potential drawbacks. These biases affect the lives and livelihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash cannot be underestimated. Here, we outline some of the sociodemographic, training data, and algorithmic biases that undermine sound health care risk assessment and medical decision-making that should be addressed in the health care system. We present a perspective and overview of these biases by gender, race, ethnicity, age, historically marginalized communities, algorithmic bias, biased evaluations, implicit bias, selection/sampling bias, socioeconomic status biases, biased data distributions, cultural biases and insurance status bias, conformation bias, information bias and anchoring biases and make recommendations to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward.