The promise of highly personalized oncology care using artificial intelligence (AI) technologies has been forecasted since the emergence of the field. Cumulative advances across the science are bringing this promise to realization, including refinement of machine learning– and deep learning algorithms; expansion in the depth and variety of databases, including multiomics; and the decreased cost of massively parallelized computational power. Examples of successful clinical applications of AI can be found throughout the cancer continuum and in multidisciplinary practice, with computer vision–assisted image analysis in particular having several U.S. Food and Drug Administration–approved uses. Techniques with emerging clinical utility include whole blood multicancer detection from deep sequencing, virtual biopsies, natural language processing to infer health trajectories from medical notes, and advanced clinical decision support systems that combine genomics and clinomics. Substantial issues have delayed broad adoption, with data transparency and interpretability suffering from AI’s “black box” mechanism, and intrinsic bias against underrepresented persons limiting the reproducibility of AI models and perpetuating health care disparities. Midfuture projections of AI maturation involve increasing a model’s complexity by using multimodal data elements to better approximate an organic system. Far-future positing includes living databases that accumulate all aspects of a person’s health into discrete data elements; this will fuel highly convoluted modeling that can tailor treatment selection, dose determination, surveillance modality and schedule, and more. The field of AI has had a historical dichotomy between its proponents and detractors. The successful development of recent applications, and continued investment in prospective validation that defines their impact on multilevel outcomes, has established a momentum of accelerated progress.

Open clavicle fractures are rare injuries. Patients often have associated head, thoracic, and great vessel injuries. Penetrating injuries have higher rates of great vessel injuries and that blunt force injuries have higher rates of head injuries.

Corticosteroids are commonly associated with changes in mood, memory, and the hippocampus. Declarative memory decline occurs rapidly after corticosteroid administration. Minimal research has focused on interventions to prevent or reverse corticosteroid effects on the human brain and associated adverse psychiatric effects. Acetaminophen has neuroprotective properties in animal models. We examined acetaminophen add-on therapy in patients prescribed corticosteroids. Thirty outpatients prescribed oral high-dose prednisone therapy for asthma (n = 28) or allergic rhinitis (n = 2) were randomized to approximately 7 days of acetaminophen (4000 mg/day) or placebo in a double-blind fashion at the same time as prednisone. Mood was assessed with the Hamilton Rating Scale for Depression, Young Mania Rating Scale, and Activation subscale of the Internal State Scale. Memory was assessed with the Rey Auditory Learning Test and asthma symptoms with the Asthma Control Questionnaire. Between-group differences were assessed using mixed ANCOVAs and within-group changes were examined with paired t-tests. Baseline mean depression scores were elevated. In the total sample, depressive and asthma symptoms improved significantly, while declarative memory worsened during prednisone therapy. No between treatment-group differences were found in mood or memory measures. Change in asthma symptoms with receiving prednisone was not related to change in mood or memory. Prednisone therapy was associated with a reduction in depressive symptom severity and decline in declarative memory that was not related to changes in asthma symptoms. This is consistent with prior research suggesting that prednisone impairs memory and may have antidepressant properties. Acetaminophen did not attenuate corticosteroid-induced mood or memory changes.

Background Excessive corticosteroid exposure is associated with atrophic effects on the human hippocampus and amygdala. These effects appear to be, at least in part, mediated through corticosteroid-induced release of glutamate. We previously reported that lamotrigine, a glutamate-release inhibitor, significantly improved declarative memory but did not change hippocampal volume, as compared to placebo, in corticosteroid-treated patients. To our knowledge, no data are available on preventing or reversing the impact of corticosteroids on the amygdala. Methods We examined the effects of 24 weeks of randomized, placebo-controlled lamotrigine therapy on amygdala volume and mood in 28 corticosteroid-treated patients (n = 12 for placebo, n = 16 for lamotrigine). Amygdala volumes were measured from tracings of the MR images from weeks 0 and 24. Mood was assessed every two weeks with Hamilton Depression Rating Scale (HAM-D) and Young Mania Rating Scale (YMRS). Results An ANCOVA revealed that patients on lamotrigine had significantly larger left amygdala volume at week 24 than patients on placebo after controlling for baseline volume. Neither exit nor week 24 ANCOVAs of HAM-D and YMRS revealed significant difference between lamotrigine and placebo groups. Conclusions Results suggest that lamotrigine attenuated the effects of corticosteroids on the left amygdala. Larger trials are warranted to confirm these findings.

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Objectives: To compare breast density assessments between C-View™ and Intelligent 2D™, different generations of synthesized mammography (SM) from Hologic. Methods: In this retrospective study, we identified a subset of females between March 2017 and December 2019 who underwent screening digital breast tomosynthesis (DBT) with C-View followed by DBT with Intelligent 2D. Clinical BI-RADS breast density was obtained along with volumetric breast density measures (including density grade, breast volume, percentage volumetric density, dense volume) using VolparaTM. Differences in density measures by type of synthesized image were calculated using the pairwise t-test or McNemar’s test, as appropriate. Results: Sixty-seven patients (avg age 62.7; range 40–84) were included with an average of 13.3 months between the two exams. No difference was found in BI-RADS density between the SM reconstructions (p = 0.74). Similarly, there was no difference in VolparaTM mean density grade (p = 0.71), mean breast volume (p = 0.48), mean dense volume (p = 0.43) or mean percent volumetric density (p = 0.12) between the exams. Conclusions: We found no significant differences in clinical and automated breast density assessments between these two versions of SM. Advances in knowledge: Lack of differences in density estimates between the two SM reconstructions is important as density assignment impacts risk stratification and adjunct screening recommendations.

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