AIMC Topic: Checklist

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Enhancing Clinical Decision Support: A Heuristic Evaluation of Explainable AI in Healthcare Dashboards.

Studies in health technology and informatics
Explainable Artificial Intelligence (XAI) is crucial for enhancing transparency, interpretability and actionability of AI systems, particularly in healthcare. The SAD XAI Dashboard, a clinical decision support (CDS) tool for sepsis-associated deliriu...

State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
AIMS: Artificial intelligence (AI) has the potential to transform cardiac electrophysiology (EP), particularly in arrhythmia detection, procedural optimization, and patient outcome prediction. However, a standardized approach to reporting and underst...

Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update.

Radiology. Artificial intelligence
To address the rapid evolution of artificial intelligence in medical imaging, the authors present the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) 2024 Update.

Deliberate Problem-solving with a Large Language Model as a Brainstorm Aid Using a Checklist for Prompt Generation.

The Journal of the Association of Physicians of India
Large language models (LLMs) use autoregression to generate text in response to queries. Crafting an appropriate prompt to elicit the desired response from these generative artificial intelligence (AI) models to solve a clinical problem can be a chal...

Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognised reporting standards.

BMJ health & care informatics
INTRODUCTION: Amid clinicians' challenges in staying updated with medical research, artificial intelligence (AI) tools like the large language model (LLM) ChatGPT could automate appraisal of research quality, saving time and reducing bias. This study...

Machine Learning Methods in Health Economics and Outcomes Research-The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
Advances in machine learning (ML) and artificial intelligence offer tremendous potential benefits to patients. Predictive analytics using ML are already widely used in healthcare operations and care delivery, but how can ML be used for health economi...

Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology: CLEAR Derm Consensus Guidelines From the International Skin Imaging Collaboration Artificial Intelligence Working Group.

JAMA dermatology
IMPORTANCE: The use of artificial intelligence (AI) is accelerating in all aspects of medicine and has the potential to transform clinical care and dermatology workflows. However, to develop image-based algorithms for dermatology applications, compre...

A systematic review on natural language processing systems for eligibility prescreening in clinical research.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We conducted a systematic review to assess the effect of natural language processing (NLP) systems in improving the accuracy and efficiency of eligibility prescreening during the clinical research recruitment process.

Review of study reporting guidelines for clinical studies using artificial intelligence in healthcare.

BMJ health & care informatics
High-quality research is essential in guiding evidence-based care, and should be reported in a way that is reproducible, transparent and where appropriate, provide sufficient detail for inclusion in future meta-analyses. Reporting guidelines for vari...

Clinician checklist for assessing suitability of machine learning applications in healthcare.

BMJ health & care informatics
Machine learning algorithms are being used to screen and diagnose disease, prognosticate and predict therapeutic responses. Hundreds of new algorithms are being developed, but whether they improve clinical decision making and patient outcomes remains...