Clinician Trust in Artificial Intelligence: What is Known and How Trust Can Be Facilitated.

Predictive analytics based on artificial intelligence (AI) offer clinicians the opportunity to leverage big data available in electronic health records (EHR) to improve clinical decision-making, and thus patient outcomes. Despite this, many barriers exist to facilitating trust between clinicians and AI-based tools, limiting its current impact. Potential solutions are available at both the local and national level.

Prospective CT screening for lung cancer in a high-risk population: HIV-positive smokers.

Background: Epidemiological evidence suggests that HIV-infected individuals are at increased risk of lung cancer, but no data exist because large computed tomography (CT) screening trials routinely exclude HIV-infected participants.

Methods: From 2006 to 2013, we conducted the world's first lung cancer screening trial of 224 HIV-infected current/former smokers to assess the CT detection rates of lung cancer. We also used 130 HIV-infected patients with known lung cancer to determine radiographic markers of lung cancer risk using multivariate analysis.

Staging lymph node metastases from lung cancer in the mediastinum.

Background: The presence of tumor metastases in the mediastinum is one of the most important elements in determining the optimal treatment strategy in patients with non-small cell lung cancer. This review is aimed at examining the current strategies for investigating lymph node metastases corresponding to an "N2" classification delineated by The International Staging Committee of the International Association for the Study of Lung Cancer (IASLC).

Methods: Extensive review of the existing scientific literature related to the investigation of mediastinal lymph node metastases was undertaken in order to summarize and report current best practices.