AIMC Topic: Diagnosis

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Ten years on: how far have we come in patient engagement in diagnosis?

Diagnosis (Berlin, Germany)
The 2015 National Academy of Sciences, Engineering and Medicine report, Improving Diagnosis in Medicine, is known for its inclusive approach to patients. This paper explores the evolution of research in patient engagement in diagnosis over the past d...

Towards conversational diagnostic artificial intelligence.

Nature
At the heart of medicine lies physician-patient dialogue, where skillful history-taking enables effective diagnosis, management and enduring trust. Artificial intelligence (AI) systems capable of diagnostic dialogue could increase accessibility and q...

The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions.

BMC medical informatics and decision making
Explainable Artificial Intelligence (XAI) enhances transparency and interpretability in AI models, which is crucial for trust and accountability in healthcare. A potential application of XAI is disease prediction using various data modalities. This s...

Assessment of Real-Time Natural Language Processing for Improving Diagnostic Specificity: A Prospective, Crossover Exploratory Study.

Applied clinical informatics
BACKGROUND:  Reliable, precise, timely, and clear documentation of diagnoses is difficult. Poor specificity or the absence of diagnostic documentation can lead to decreased revenue and increased payor denials, audits, and queries to providers. Nuance...

Explanatory argumentation in natural language for correct and incorrect medical diagnoses.

Journal of biomedical semantics
BACKGROUND: A huge amount of research is carried out nowadays in Artificial Intelligence to propose automated ways to analyse medical data with the aim to support doctors in delivering medical diagnoses. However, a main issue of these approaches is t...

One-dimensional convolutional neural network-based active feature extraction for fault detection and diagnosis of industrial processes and its understanding via visualization.

ISA transactions
Feature extraction from process signals enables process monitoring models to be effective in industrial processes. Deep learning presents extensive possibilities for extracting abstract features from image and visual data. However, the main inputs of...

Closing the translation gap: AI applications in digital pathology.

Biochimica et biophysica acta. Reviews on cancer
Recent advances in artificial intelligence show tremendous promise to improve the accuracy, reproducibility, and availability of medical diagnostics across a number of medical subspecialities. This is especially true in the field of digital pathology...