Use of large language model-based chatbots in managing the rehabilitation concerns and education needs of outpatient stroke survivors and caregivers.

Background: The utility of large language model-based (LLM) artificial intelligence (AI) chatbots in many aspects of healthcare is becoming apparent though their ability to address patient concerns remains unknown. We sought to evaluate the performance of two well-known, freely-accessible chatbots, ChatGPT and Google Bard, in responding to common questions about stroke rehabilitation posed by patients and their caregivers. Methods: We collected questions from outpatients and their caregivers through a survey, categorised them by theme, and created representative questions to be posed to both chatbots. We then evaluated the chatbots' responses based on accuracy, safety, relevance, and readability. Interrater agreement was also tracked. Results: Although both chatbots achieved similar overall scores, Google Bard performed slightly better in relevance and safety. Both provided readable responses with some general accuracy, but struggled with hallucinated responses, were often not specific, and lacked awareness of the possibility for emotional situations with the potential to turn dangerous. Additionally, interrater agreement was low, highlighting the variability in physician acceptance of their responses. Conclusions: AI chatbots show potential in patient-facing support roles, but issues remain regarding safety, accuracy, and relevance. Future chatbots should address these problems to ensure that they can reliably and independently manage the concerns and questions of stroke patients and their caregivers.

A Case report of Becker Muscular Dystrophy and Stroke who successfully regained Mobility with Robot Assisted Gait Training.

ABSTRACT: A 30-year-old patient with Becker Muscular Dystrophy presented with stroke. Background issues of proximal weakness, dilated cardiomyopathy and reduced endurance challenged the usual goal-setting and formulation of a stroke rehabilitation plan. We discuss the holistic rehabilitation program that this patient underwent, with a focus on the utilization of robot-assisted gait training that eventually led him to successfully regain mobility.

Novel use of robot-assisted gait rehabilitation in a patient with stroke and blindness.

Robot-assisted gait training (RAGT) is an effective adjunctive treatment for patients with stroke that helps to regain functional mobility and is applied in many rehabilitation units for poststroke neurorecovery. We discuss our successful attempt to apply RAGT in a patient with blindness that impeded his ability to maintain balance during gait training. He initially required two assistants to walk, but after undergoing conventional therapy with adjunctive RAGT, he improved to standby assistance for ambulation. There were also improvements in balance, activity tolerance and quality of life. Low-or-no vision states can affect the pace of recovery poststroke, but RAGT and conventional physiotherapy can possibly be combined in such patients to improve balance and motor outcomes. The Andago robot's safety features of weight support, harnessed suspension and walking mode selection supported our decision and enabled us to apply it safely for this patient.