Medical oncology
-
Cancer patients are at particular risk from COVID-19 since they usually present multiple risk factors for this infection such as older age, immunosuppressed state, comorbidities (e.g., chronic lung disease, diabetes, cardiovascular diseases), need of frequent hospital admissions and visits. Therefore, in the COVID era, oncologists should carefully weigh risks/benefits when planning cancer therapies and follow-up appointments. Recently, several scientific associations developed specific guidelines or recommendations to help physicians in their clinical practice. This review focuses on main available guidelines/recommendations regarding the cancer patient management during the COVID-19 pandemic.
-
We here express our concern about a general decree to let patients wear face masks in radiation oncology clinics. We believe that potential risks associated with wearing masks, such as the risk of confounding patients, outweigh any benefits of such a policy for which evidence of protection from COVID-19 is generally weak. For asymptomatic patients, wearing masks in addition to hygiene standards will not provide additional protection of others and should be cautioned against.
-
Currently world is fighting with global pandemic of coronavirus disease 2019 (COVID-19). At this time of uncertainty, oncologists are struggling to provide appropriate care to cancer patients. ⋯ As cancer patients are immunocompromised and there are high chances of exposure during hospital visits and if they get infected, outcome can be fatal. So through the column of this article, we would like to provide basic guideline in management of cancer patients during COVID-19 pandemic.
-
Artificial intelligence (AI) is revolutionizing healthcare and transforming the clinical practice of physicians across the world. Radiology has a strong affinity for machine learning and is at the forefront of the paradigm shift, as machines compete with humans for cognitive abilities. AI is a computer science simulation of the human mind that utilizes algorithms based on collective human knowledge and the best available evidence to process various forms of inputs and deliver desired outcomes, such as clinical diagnoses and optimal treatment options. ⋯ Concerns have been expressed reflecting opinions that future medicine based on AI will render radiologists irrelevant. Thus, how much of this is based on reality? To answer these questions, it is important to examine the facts, clarify where AI really stands and why many of these speculations are untrue. We aim to debunk the 6 top myths regarding AI in the future of radiologists.