Artificial Intelligence for Remote Monitoring and Management of Chronic Rheumatological Diseases: A Case Study of Osteoarthritis and Rheumatoid Arthritis

Authors

  • Sahil Kumar
  • Hanaa Khalil
  • Ahmed Raheem Rayshan
  • Nolka J Omana C.
  • Anirudh Gupta
  • Azza M. Atya
  • Ahmad Habahbeh
  • Prof. Dr. Sudhair Abbas Bangash

DOI:

https://doi.org/10.69980/ajpr.v27i2.414

Keywords:

Quantitative Study, Digital Health Tools, Statistical Analysis, Tool Effectiveness, Patient Outcomes

Abstract

Background: Healthcare has been transformed by Artificial Intelligence (AI) which demonstrates remote monitoring capability for assessing and treating patients with chronic rheumatological diseases including osteoarthritis (OA) and rheumatoid arthritis (RA). Fortunately, AI has shown much promise yet its real-world performance along with user outcomes remain poorly documented.

Objective: This research evaluates the capabilities of artificial intelligence in treating osteoarthritis and rheumatoid arthritis by examining user evaluations of program performance and determining elements affecting satisfaction with AI systems.

Methods: The quantitative study utilized structured surveys with three distinct target groups - patients and healthcare providers alongside researchers. The survey monitored participant demographics alongside survey data regarding disease profiles together with digital tool behaviors and evaluations of Artificial Intelligence beneficial attributes and system happiness. Statistical analysis consisted of descriptive along with inferential measures plus Shapiro-Wilk tests for normality verification together with Cronbach's Alpha calculations for reliability checks followed by linear regression to evaluate the relationship between tool efficiency and satisfaction.

Results: The study included 250 respondents. Using the chi-square test, it was established that the normality tests that were employed to check the normal distribution of “Tool Effectiveness” and “Satisfaction” scores were both rejected at 0.05 significant levels. The internal consistency of the reliabilities was low (Cronbach’s Alpha = 0.178). Further, regression analysis displayed a poor negative correlation where tool effectiveness could explain only 1% of satisfaction with a value of p > 0.05. The graphics used showed a variation in responses and recommended a broad-based approach to the assessment.

Conclusion: Currently, overall OA and RA management with AI-based tools can be perceived as promising; however, the current level of user satisfaction and perceived effectiveness in designing and implementing those tools seems to be questionable. Thus, the subsequent promotion experiences should focus on aspects such as comprehensibility, credibility, and infrastructure within medical organizations. Future studies are required to better optimize AI interventions and understand AI’s overall effect on the control and management of the studied diseases.

Author Biographies

Sahil Kumar

Master of Science in Business Analytics, DePaul University, Chicago, IL, USA

Hanaa Khalil

Pediatric Specialist, Master Degree In Pediatric, Imam Abdulrahman Alfaisal Hospital

Ahmed Raheem Rayshan

Department of physiology, pharmacology and Biochemistry, college of veterinary medicine, University of Al-Qadisiyah, Al-Diwaniyah / Iraq

Nolka J Omana C.

Dentist (University of The Andes, Venezuela) MSc in Oral Biology (University of Carabobo, Venezuela), MSc in Oral Pathology Central University, Venezuela)

Anirudh Gupta

Assistant Professor, Department of Biotechnology, NIMS Institute of Allied Medical Science and Technology, NIMS University Rajasthan, India

Azza M. Atya

Rehabilitation Sciences Department, Health and Rehabilitation Sciences College, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

Ahmad Habahbeh

Resident, Department of Orthopedic Surgery, Marienhospital Wesel, Academic Teaching Hospital of the University of Münster, Wesel, Germany

Prof. Dr. Sudhair Abbas Bangash

Faculty of management sciences, Department of Business Administration, Sarhad University of Science and Information Technology, Peshawar, Pakistan

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Published

2024-12-29

Issue

Vol. 27 No. 2 (2024): American Journal of Psychiatric Rehabilitation

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Articles

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