Integrating Artificial Intelligence For Personalized Cancer Pain Management: A Comprehensive Study On Healthcare Professionals’ Perspectives, Development Of Pain Detection Method And User’s Acceptance

Tashtoush, Noor Hasan Ibrahim (2025) Integrating Artificial Intelligence For Personalized Cancer Pain Management: A Comprehensive Study On Healthcare Professionals’ Perspectives, Development Of Pain Detection Method And User’s Acceptance. PhD thesis, Universiti Sains Malaysia.

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Abstract

Cancer pain is a prevalent and distressing symptom that significantly impairs the quality of life in oncology patients. Traditional pain assessments rely heavily on subjective self-reporting, which may be limited by communication, or cognitive impairment. With the rise of wearable technologies, smartwatches offer a promising avenue for non-invasive, continuous monitoring of physiological signals associated with pain, enabling more objective assessment and real-time support. This research explores the integration of Artificial Intelligence (AI) in personalized cancer pain management across three phases. Phase I investigated healthcare professionals’ Knowledge, Attitudes, and Practices (KAP) and barriers regarding cancer pain and AI tool adoption through semi-structured interviews with thirteen purposively selected doctors and nurses from Advanced Medical and Dental Institute. In-depth interviews revealed strong understanding of conventional pain assessment methods and openness to AI-supported solutions. However, concerns about trust, training, and infrastructure emerged as critical barriers to implementation. Phase II focused on the development and validation of AI models for objective pain classification based on continuous, real-time physiological data (heart rate, respiratory rate, and blood pressure) collected via smartwatches over a 72-hour period.

Item Type: Thesis (PhD)
Subjects: R Medicine > RS Pharmacy and materia medica > RS1-441 Pharmacy and materia medica
Divisions: Pusat Pengajian Sains Farmasi (School of Pharmacy) > Thesis
Depositing User: Mr Aizat Asmawi Abdul Rahim
Date Deposited: 24 Jul 2026 01:51
Last Modified: 24 Jul 2026 01:51
URI: http://eprints.usm.my/id/eprint/64711

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