AIMC Topic: Clinical Decision-Making

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An Approach for Combining Clinical Judgment with Machine Learning to Inform Medical Decision Making: Analysis of Nonemergency Surgery Strategies for Acute Appendicitis in Patients with Multiple Long-Term Conditions.

Medical decision making : an international journal of the Society for Medical Decision Making
BACKGROUND: Machine learning (ML) methods can identify complex patterns of treatment effect heterogeneity. However, before ML can help to personalize decision making, transparent approaches must be developed that draw on clinical judgment. We develop...

Artificial Intelligence for Clinical Decision-Making: Gross Negligence Manslaughter and Corporate Manslaughter.

The New bioethics : a multidisciplinary journal of biotechnology and the body
This paper discusses the risk of gross negligence manslaughter (GNM) and corporate manslaughter charges (CM) when clinicians use an artificially intelligent system's (AIS's) outputs in their practice. I identify the elements of these offenses within ...

Navigating ChatGPT's alignment with expert consensus on pediatric OSA management.

International journal of pediatric otorhinolaryngology
OBJECTIVE: This study aimed to evaluate the potential integration of artificial intelligence (AI), specifically ChatGPT, into healthcare decision-making, focusing on its alignment with expert consensus statements regarding the management of persisten...

[AI-supported decision-making in obstetrics - a feasibility study on the medical accuracy and reliability of ChatGPT].

Zeitschrift fur Geburtshilfe und Neonatologie
The aim of this study is to investigate the feasibility of artificial intelligence in the interpretation and application of medical guidelines to support clinical decision-making in obstetrics. ChatGPT was provided with guidelines on specific obstetr...

Sensor technology and machine learning to guide clinical decision making in plastic surgery.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS
Subjective clinical evaluations are deeply rooted in medical practice. Recent advances in sensor technology facilitate the acquisition of extensive amounts of objective physiological data that can serve as a surrogate for subjective assessments. Alon...

Improving Clinical Decision Making With a Two-Stage Recommender System.

IEEE/ACM transactions on computational biology and bioinformatics
Clinical decision-making is complex and time-intensive. To help in this effort, clinical recommender systems (RS) have been designed to facilitate healthcare practitioners with personalized advice. However, designing an effective clinical RS poses ch...

Deep Reinforcement Learning for personalized diagnostic decision pathways using Electronic Health Records: A comparative study on anemia and Systemic Lupus Erythematosus.

Artificial intelligence in medicine
BACKGROUND: Clinical diagnoses are typically made by following a series of steps recommended by guidelines that are authored by colleges of experts. Accordingly, guidelines play a crucial role in rationalizing clinical decisions. However, they suffer...

Explanatory argument extraction of correct answers in resident medical exams.

Artificial intelligence in medicine
Developing technology to assist medical experts in their everyday decision-making is currently a hot topic in the field of Artificial Intelligence (AI). This is specially true within the framework of Evidence-Based Medicine (EBM), where the aim is to...

Impact of a deep learning-based brain CT interpretation algorithm on clinical decision-making for intracranial hemorrhage in the emergency department.

Scientific reports
Intracranial hemorrhage is a critical emergency that requires prompt and accurate diagnosis in the emergency department (ED). Deep learning technology can assist in interpreting non-enhanced brain CT scans, but its real-world impact on clinical decis...