Development, validation, and feature extraction of a deep learning model predicting in-hospital mortality using Japan's largest national ICU database: a validation framework for transparent clinical Artificial Intelligence (cAI) development.

Objective: While clinical Artificial Intelligence (cAI) mortality prediction models and relevant studies have increased, limitations including the lack of external validation studies and inadequate model calibration leading to decreased overall accuracy have been observed. To combat this, we developed and evaluated a novel deep neural network (DNN) and a validation framework to promote transparent cAI development.

Methods: Data from Japan's largest ICU database was used to develop the DNN model, predicting in-hospital mortality including ICU and post-ICU mortality by days since ICU discharge.

Comparison of Disease Patterns and Outcomes Between Non-Japanese and Japanese Patients at a Single Tertiary Emergency Care Center in Japan.

Background: Japan's historically low immigration rate and monolingual culture makes it a particularly interesting setting for clarifying non-national medical care. Our study objective was to examine disease patterns and outcome differences between Japanese and non-Japanese patients in a rapidly globalizing nation.

Methods: A secondary data analysis of 325 non-Japanese and 13,370 Japanese patients requiring tertiary care or intensive-care unit or high-care unit admission to the emergency department at the Tokyo Medical and Dental University medical hospital from 2010 through 2019 was conducted.

The advent of medical artificial intelligence: lessons from the Japanese approach.

Artificial intelligence or AI has been heralded as the most transformative technology in healthcare, including critical care medicine. Globally, healthcare specialists and health ministries are being pressured to create and implement a roadmap to incorporate applications of AI into care delivery. To date, the majority of Japan's approach to AI has been anchored in industry, and the challenges that have occurred therein offer important lessons for nations developing new AI strategies.