AIMC Topic: Patient Preference

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An augmented preference-based Bayesian approach for optimizing neuromodulation stimulation parameters using meta learning.

Journal of neural engineering
Electrical neuromodulation is increasingly used in the treatment of neurological disorders; however, the selection of stimulation parameters that provide optimal therapeutic benefits remains a major challenge. Moreover, identifying pathological bioma...

An intuitionistic fuzzy automated negotiation model for personalized and efficient shared decision-making.

Scientific reports
Shared decision-making (SDM) is a healthcare decision-making model that integrates patient preferences with medical expertise. It seeks to foster active involvement, enhance satisfaction, and strengthen the doctor-patient relationship. However, SDM a...

Patient preference predictors revisited: technically feasible, ethically desirable, yet must be clinically relevant.

Critical care (London, England)
Although goal-concordant care is central to patient-centered medicine, determining treatment preferences for incapacitated patients remains a challenge. Nearly two decades ago, algorithms were proposed to estimate the most likely treatment preference...

Preferences of Patients With Tuberculosis for AI-Assisted Remote Health Management: Discrete Choice Experiment.

Journal of medical Internet research
BACKGROUND: Tuberculosis remains a major global public health challenge, especially in low-resource settings where long-term treatment adherence and regular follow-up are critical. The integration of artificial intelligence (AI) into remote health ma...

Patient Perceptions of Artificial Intelligence and Telemedicine in Dermatology: Narrative Review.

JMIR dermatology
BACKGROUND: Artificial intelligence (AI) and telemedicine have significant potential to transform dermatology care delivery, but patient perspectives on these technologies have not been systematically compared.

Exploring tuberculosis patients' preferences for AI-assisted remote health management services in China: a protocol for a discrete choice experiment.

BMJ open
INTRODUCTION: Effective health management is critical for patients with tuberculosis (TB), especially given the need for long-term treatment adherence and continuous monitoring. Artificial intelligence (AI)-assisted remote health management services ...

It is not about autonomy: realigning the ethical debate on substitute judgement and AI preference predictors in healthcare.

Journal of medical ethics
This article challenges two dominant assumptions in the current ethical debate over the use of algorithmic Personalised Patient Preference Predictors (P4) in substitute judgement for incapacitated patients. First, I question the belief that the auton...

Preferences for Telephone Cancer Information and Support in People with Cancer and Carers: Attribute and Level Selection for a Discrete Choice Experiment.

The patient
BACKGROUND AND OBJECTIVE: Telephone cancer information and support services (CISS) deliver essential evidence-based resources for people living with cancer. This research aimed to describe how attributes and levels were developed for a future discret...

Young Adult Perspectives on Artificial Intelligence-Based Medication Counseling in China: Discrete Choice Experiment.

Journal of medical Internet research
BACKGROUND: As artificial intelligence (AI) permeates the current society, the young generation is becoming increasingly accustomed to using digital solutions. AI-based medication counseling services may help people take medications more accurately a...

Virtual Patients Using Large Language Models: Scalable, Contextualized Simulation of Clinician-Patient Dialogue With Feedback.

Journal of medical Internet research
BACKGROUND: Virtual patients (VPs) are computer screen-based simulations of patient-clinician encounters. VP use is limited by cost and low scalability.