AIMC Topic: Clinical Decision-Making

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Multi-task reinforcement learning and explainable AI-Driven platform for personalized planning and clinical decision support in orthodontic-orthognathic treatment.

Scientific reports
This study presents a novel clinical decision support platform for orthodontic-orthognathic treatment that integrates multi-task reinforcement learning with explainable artificial intelligence. The platform addresses the challenges of personalized tr...

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...

A narrative review of the use of PROMs and machine learning to impact value-based clinical decision-making.

BMC medical informatics and decision making
PURPOSE: This review summarises the studies which combined Patient Reported Outcome Measures (PROMs) and Machine Learning statistical computational techniques, to predict patient post-intervention outcomes. The aim of the project was to inform those ...

Perspectives of Health Care Professionals on the Use of AI to Support Clinical Decision-Making in the Management of Multiple Long-Term Conditions: Interview Study.

Journal of medical Internet research
BACKGROUND: Managing multiple long-term conditions (MLTC) is complex. Clinical management guidelines are typically focused on individual conditions and lack a robust evidence base for patients with MLTC. MLTC management is largely delivered in primar...

Artificial Intelligence in Clinical Nutrition: Bridging Data Analytics and Nutritional Care.

Current nutrition reports
PURPOSE OF REVIEW: This review explores how artificial intelligence can help advance clinical nutrition and address nutrition education and practice challenges. It highlights the role of AI, mainly through advanced clinical decision-making using gene...

Innovative AI models for clinical decision-making: predicting blastocyst formation and quality from time-lapse embryo images up to embryonic day 3.

Computers in biology and medicine
Accurate embryo assessment on embryonic day 3 of assisted reproductive technology (ART) is crucial for deciding whether to continue the culture until day 5 (blastocyst stage) or opt for earlier transfer or cryopreservation. Prolonged culture often im...

Artificial intelligence in muscle-invasive bladder cancer: opportunities, challenges, and clinical impact.

Current opinion in urology
PURPOSE OF REVIEW: Muscle-invasive bladder cancer (MIBC) represents an aggressive malignancy with significant morbidity and mortality. Recent advances in artificial intelligence (AI) offer promising opportunities to enhance patient care across the en...

Toward responsible artificial intelligence in medicine: Reflections from the Australian epilepsy project.

Artificial intelligence in medicine
Artificial intelligence (AI) is a multidisciplinary scientific field that uses machines to solve real-world problems and predict outcomes. Despite the current enthusiasm about AI's potential as a clinical support tool, there is also a growing awarene...

Impact of artificial intelligence assistance on diagnosing periapical radiolucencies: A randomized controlled trial.

Journal of dentistry
OBJECTIVES: This randomized controlled trial aimed to evaluate the impact of artificial intelligence (AI) assistance on dentists' diagnostic accuracy, confidence, and treatment decisions when detecting periapical radiolucencies (PRs) on panoramic rad...

Exploring the medical ethical limitations of GPT-4 in clinical decision-making scenarios: a pilot survey.

Frontiers in public health
BACKGROUND: This study aims to conduct an examination of GPT-4's tendencies when confronted with ethical dilemmas, as well as to ascertain their ethical limitations within clinical decision-makings.