A recently introduced list of operational indicators of consciousness, originally developed for challenging cases like non-human animals and artificial intelligence, may help address the high misdiagnosis rate among patients with disorders of consciousness. These indicators are particular capacities deduced from behavior, cognitive performance, or neural correlates, and they do not define a hard threshold for consciousness but allow graded inference based on consistency across indicators. Applying these indicators to disorders of consciousness could inspire new strategies for reducing misdiagnosis, establishing a gold standard for detecting consciousness, and refining the taxonomy of these disorders.
Computer models and simulations are increasingly used to describe, explain, and predict brain function, aiming to integrate fragmented neuroscientific knowledge. This paper examines whether simulation technologies could plausibly emulate consciousness and assesses their potential clinical impact on disorders of consciousness such as coma, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state. Despite technical limitations, the authors suggest that simulating neural correlates of consciousness may offer new solutions to practical clinical problems, particularly improving treatments for patients with these disorders.