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The integration of Artificial Intelligence (AI) in business settings is rapidly increasing, yet significant limitations hinder its effective use and adoption. Understanding these limitations and their interrelationships is crucial for enhancing AI implementation. Despite growing research, there is a lack of a comprehensive model that systematically identifies and elucidates the factors influencing AI limitations in business environments. This study employs Interpretive Structural Modeling (ISM), combined with MICMAC analysis and an extensive literature review, to develop such a model. We identified 15 key factors and analyzed their driving and dependence powers to understand their interrelationships. Most factors, such as contextual understanding, transparency, intuition, emotional intelligence, ethics, bias, tacit knowledge, creativity, credibility, and reliability, were found to be autonomous. Accountability and privacy emerged as the strongest driving forces, while trust and adaptability exhibited the highest dependence and lowest driving power. This research offers a comprehensive understanding of AI limitations and their interrelationships, providing valuable insights for managers and businesses. The findings can aid in making more informed decisions about AI implementation and in developing strategies to mitigate these limitations. Furthermore, the study emphasizes the importance of combining AI with human insight to overcome these challenges. However, using the ISM technique could involve subjective judgment from the experts.

IntroductionThe Coronavirus Disease 2019 (COVID-19) pandemic posed critical challenges in providing care to ovarian cancer (OC) patients, including delays in OC diagnosis and treatment initiation. To accommodate for delays in OC surgery, the Society of Gynecologic Oncology (SGO) recommended preferential use of neoadjuvant chemotherapy during the pandemic. The purpose of this study was to assess the association of the COVID-19 pandemic with neoadjuvant chemotherapy use in patients diagnosed with OC.MethodsThis retrospective cohort study included patients diagnosed with stage II-IV ovarian cancer of epithelial subtype between 01/01/2017-06/30/2021 at Kaiser Permanente Southern California (KPSC), a large integrated healthcare system in the United States. Ovarian cancer patients diagnosed between 2017-2020 were identified from KPSC’s Surveillance, Epidemiology, and End Results (SEER)-affiliated cancer registry. Patients diagnosed in 2021 were identified from the electronic medical records (EMR) using ICD-10 diagnosis codes, followed by medical chart review to validate diagnosis and extract information on histology and stage at diagnosis. March 4, 2020 was used as the cut-off to define pre-pandemic and pandemic periods. Patients diagnosed with COVID-19 between OC diagnosis and treatment completion were excluded. Data on neoadjuvant chemotherapy use were extracted from the cancer registry and EMR, supplemented by chart review. Modified Poisson regression was used to evaluate the association of the pandemic with neoadjuvant chemotherapy use.ResultsOf 566 OC patients, 160 (28.3%) were diagnosed in the pandemic period. Patients diagnosed in the pandemic period were slightly younger (mean age 62.7 vs 64.9 years, p=0.07) and had a higher burden of Charlson comorbidities (p=0.05) than patients diagnosed in pre-pandemic period. No differences in time to treatment initiation were observed by pandemic periods. Neoadjuvant chemotherapy use was documented in 58.7% patients during the pandemic period compared to 47.3% in pre-pandemic period (p=0.01). After adjusting for covariates, patients diagnosed in the pandemic period were 29% more likely to receive neoadjuvant chemotherapy than patients diagnosed in pre-pandemic period [RR(95%CI): 1.29(1.12-1.49)].DiscussionsOvarian cancer patients diagnosed in the COVID-19 pandemic were more likely to receive neoadjuvant chemotherapy than patients diagnosed before the pandemic. Future research on patient outcomes and trends in the post-pandemic period are warranted.

Uterine leiomyosarcoma (uLMS) is an aggressive mesenchymal tumor associated with a poor prognosis. Research demonstrates that PARP inhibitors (PARPi) improve disease-stable survival in patients with somatic BRCA1/2 mutations through the process of synthetic lethality. Therefore, PARPi’s may have a role in treating gynecologic malignancies with deleterious BRCA1/2 mutations. This patient is a 50-year-old female with a history of stage IB uterine leiomyosarcoma, complicated by recurrence along the vaginal cuff and metastases to the lungs. A somatic BRCA2 mutation was identified, and the patient was started on Olaparib for treatment of recurrent disease. The patient has now been disease free for two years. We recommend next generation sequencing be performed to identify functional BRCA1/2 loss in uLMS as PARPi may be a potential targeted therapy for uLMS.