The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare

Author:

Pashangpour Souren1ORCID,Nejat Goldie123ORCID

Affiliation:

1. Autonomous Systems and Biomechatronics Laboratory (ASBLab), Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada

2. KITE, Toronto Rehabilitation Institute, University Health Newtork (UHN), Toronto, ON M5G 2A2, Canada

3. Rotman Research Institute, Baycrest Health Sciences, North York, ON M6A 2E1, Canada

Abstract

The potential use of large language models (LLMs) in healthcare robotics can help address the significant demand put on healthcare systems around the world with respect to an aging demographic and a shortage of healthcare professionals. Even though LLMs have already been integrated into medicine to assist both clinicians and patients, the integration of LLMs within healthcare robots has not yet been explored for clinical settings. In this perspective paper, we investigate the groundbreaking developments in robotics and LLMs to uniquely identify the needed system requirements for designing health-specific LLM-based robots in terms of multi-modal communication through human–robot interactions (HRIs), semantic reasoning, and task planning. Furthermore, we discuss the ethical issues, open challenges, and potential future research directions for this emerging innovative field.

Funder

AGE-WELL Inc.

Canadian Frailty Network

Canada Research Chairs

Natural Sciences and Engineering Research Council of Canada

NSERC HeRo CREATE program

Publisher

MDPI AG

Reference277 articles.

1. World Health Organization (2024, January 03). Ageing and Health. Available online: https://www.who.int/news-room/fact-sheets/detail/ageing-and-health.

2. Hornstein, J. (2024, January 19). Chronic Diseases in America|CDC, Available online: https://www.cdc.gov/chronicdisease/resources/infographic/chronic-diseases.htm.

3. COVID-19 and Chronic Disease: The Impact Now and in the Future;Hacker;Prev. Chronic. Dis.,2021

4. (2024, January 19). Express Entry Targeted Occupations: How Many Healthcare Workers Does Canada Need?|CIC News. Available online: https://www.cicnews.com/2023/10/express-entry-targeted-occupations-how-many-healthcare-workers-does-canada-need-1040056.html.

5. (2024, June 25). Fact Sheet: Strengthening the Health Care Workforce|AHA. Available online: https://www.aha.org/fact-sheets/2021-05-26-fact-sheet-strengthening-health-care-workforce.

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