Subarachnoid hemorrhage (SAH) continues to be a leading cause of morbidity and mortality, with cerebral vasospasm as a common etiology of worse clinical progression. The purpose of this study was to evaluate and review the current literature concerning the effective treatment of SAH. The treatment options for SAH are expanding as new therapeutic targets are identified. Nimodipine is the primary medication prescribed due to its neuroprotective properties. In addition, certain drugs can enhance lymphatic flow and influence the recovery process, such as Dexmedetomidine, SSRIs, and DL-3-n-butylphthalide. Vasospastic and ischemic patients commonly undergo transluminal balloon angioplasty. Clinical trials have not yet provided conclusive evidence to support the use of magnesium or statins. Moreover, other agents such as calcium channel blockers, milrinone, hydrogen sulfide, exosomes, erythropoietin, cilostazol, fasudil, albumin, Eicosapentaenoic acid, corticosteroids, minocycline, and stellate ganglion blockade should be investigated further.
Objective: Clinical applications of machine learning are promising as a tool to improve patient outcomes through assisting diagnoses, treatment, and analyzing risk factors for screening. Possible clinical applications are especially prominent in dermatology as many diseases and conditions present visually. This allows a machine learning model to analyze and diagnose conditions using patient images and data from electronic health records (EHRs) after training on clinical datasets but could also introduce bias. Despite promising applications, artificial intelligence has the capacity to exacerbate existing demographic disparities in healthcare if models are trained on biased datasets.
Methods: Through systematic literature review of available literature, we highlight the extent of bias present in clinical datasets as well as the implications it could have on healthcare if not addressed.
Results: We find the implications are worsened in dermatological models. Despite the severity and complexity of melanoma and other dermatological diseases as well as differing disease presentations based on skin-color, many imaging datasets underrepresent certain demographic groups causing machine learning models to train on images of primarily fair-skinned individuals leaving minorities behind.
Conclusion: In order to address this disparity, research first needs to be done investigating the extent of the bias present and the implications it may have on equitable healthcare.
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