AI in Healthcare

iKang Technology Team

Nov 12, 2024 · Artificial Intelligence

AI Applications in Medical Imaging for Enhanced Disease Detection

AI-driven medical imaging leverages deep learning and massive datasets to detect lesions—often earlier than human eyes— with high accuracy and speed, reducing fatigue‑related errors and workload, while challenges such as data quality, interpretability, privacy, and regulatory compliance must be addressed for widespread clinical adoption.

AIAI applicationsAI in Healthcare

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AI Applications in Medical Imaging for Enhanced Disease Detection

Architects Research Society

May 15, 2023 · Artificial Intelligence

Rethinking Rogers' Bell Curve: Innovation Diffusion, Its Evolution, and the AI Glacier in Healthcare

The article revisits Everett Rogers' diffusion of innovations theory, examines the evolution of his bell‑curve model through S‑curves, Moore's crossing‑the‑chasm, and Normalization Process Theory, and analyzes why artificial intelligence faces a deep adoption gap in the healthcare sector.

AI in HealthcareRogers' bell curvecrossing the chasm

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Rethinking Rogers' Bell Curve: Innovation Diffusion, Its Evolution, and the AI Glacier in Healthcare

DataFunTalk

Dec 30, 2022 · Artificial Intelligence

Graph Representation Learning for Drug Package Recommendation: Discriminative and Generative Approaches

This article reviews the challenges of drug package recommendation in smart healthcare and presents two graph‑based solutions—a discriminative model (DPR) that scores existing drug packages and a generative model (DPG) that creates personalized packages—demonstrating superior performance through extensive experiments and analysis.

AI in HealthcareGenerative ModelsGraph Neural Networks

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Graph Representation Learning for Drug Package Recommendation: Discriminative and Generative Approaches

DataFunTalk

Nov 25, 2021 · Artificial Intelligence

Integrating Domain Knowledge into AI‑Driven Medical Reasoning: Trends, Challenges, and Techniques

With the rise of deep learning, the article examines the current state of smart healthcare, identifies core challenges such as industry constraints, data privacy, and algorithmic limitations, and explores how domain knowledge—through medical knowledge graphs and specialized pre‑training models—can be integrated into AI reasoning for improved clinical decision support.

AI in HealthcareClinical Decision SupportDomain Pretraining

0 likes · 17 min read

Integrating Domain Knowledge into AI‑Driven Medical Reasoning: Trends, Challenges, and Techniques