Patient Perspectives on AI-Driven Predictions of Schizophrenia Relapses: Understanding Concerns and Opportunities for Self-Care and Treatment. | LitMetric
Early detection and intervention for relapse is important in the treatment of schizophrenia spectrum disorders. Researchers have developed AI models to predict relapse from patient-contributed data like social media. However, these models face challenges, including misalignment with practice and ethical issues related to transparency, accountability, and potential harm. Furthermore, how patients who have recovered from schizophrenia view these AI models has been underexplored. To address this gap, we first conducted semi-structured interviews with 28 patients and reflexive thematic analysis, which revealed a disconnect between AI predictions and patient experience, and the importance of the social aspect of relapse detection. In response, we developed a prototype that used patients' Facebook data to predict relapse. Feedback from seven patients highlighted the potential for AI to foster collaboration between patients and their support systems, and to encourage self-reflection. Our work provides insights into human-AI interaction and suggests ways to empower people with schizophrenia.
Salivaomics has emerged as a ground-breaking field in the detection and management of oral cancer (OC), offering a non-invasive, efficient, and patient-friendly alternative to traditional diagnostic methods. This innovative approach leverages the comprehensive molecular insights provided by genomics, transcriptomics, proteomics, metabolomics, and microbiomics. The potential of salivaomics lies in its ability to enable early detection, predict malignant transformation, and monitor treatment outcomes and disease recurrence.
Small-cell lung cancer (SCLC) is a refractory cancer with rapid growth and high aggressiveness. Extensive-stage SCLC is initially sensitive to chemotherapy; however, drug resistance and recurrence occur rapidly, resulting in a poor survival outcome due to lack of subsequently efficient therapy. The emergence of immune checkpoint inhibitors (ICIs) generated a new landscape of SCLC treatment and significantly prolonged the survival of patients.
Background: In triple-negative breast cancer (TNBC), the most immunogenic breast cancer type, tumor-infiltrating lymphocytes (TILs) are an independent prognostic factor. Tertiary lymphoid structures (TLS) are an important TILs source, but they are not integrated in the current prognostic criteria.
Methods: In this retrospective study, TLS were assessed in hematein-eosin-saffron-stained (HES) histological sections from 397 early, chemotherapy-naive TNBC samples after primary surgical resection.
Background: Relapsed/Refractory (R/R) diffuse large B-cell lymphoma (DLBCL) represents a subgroup with a high incidence and dismal prognosis. Currently, there is a lack of robust models for predicting R/R DLBCL. Therefore, we conducted a retrospective study to identify key determinants to be incorporated into a novel nomogram to enhance the identification of DLBCL patients at elevated risk of refractoriness/recurrence.
Background: Tumor recurrence or metastasis after surgery is a significant factor influencing bladder cancer (BC) prognosis. Novel molecular biomarkers are necessary to determine each patient's specific outcome because current biomarkers have limited power for predicting prognosis. The proto-oncogene MET encodes c-MET, a tyrosine kinase receptor.
