Small Data Explainer -- The impact of small data methods in everyday life

Patient-centered data science: an integrative framework for evaluating and predicting clinical outcomes in the digital health era

arXiv:2408. 02677v2 Announce Type: replace-cross Abstract: This study proposes a novel, integrative framework for patient-centered data science in the digital health era.

From Overload to Insights: How AI Agents Can Support Scientists in Analyzing Complex Data

arXiv:2607. 16845v1 Announce Type: new Abstract: Scientists at European XFEL conduct experiments that generate very large and complex datasets.

How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI

arXiv:2602. 00056v4 Announce Type: replace-cross Abstract: Large-scale data has fuelled the success of frontier artificial intelligence (AI) models over the past decade.

On the missing data layer and a potential solution

arXiv:2608. 02949v1 Announce Type: new Abstract: Latin America is missing two foundational layers of AI infrastructure: the dataset layer and the benchmark layer.

Position: Stop Preaching and Start Practising Data Frugality for Responsible Development of AI

arXiv:2602. 19789v2 Announce Type: replace Abstract: This position paper argues that the machine learning community must move from preaching to practising data frugality for responsible artificial intelligence (AI) development.

Introducing AI Sheets: a tool to work with datasets using open AI models!

Many Companies Use AI. Few Know How to Build an AI-Native Enterprise Data Platform.

A practical enterprise AI architecture with data agents, AI-powered QA, and AI governance. The post Many Companies Use AI.

Improving Rural Medication Safety with AI: A Scoping Review

The scoping review examines how artificial intelligence (AI) is applied across all stages of medication management in rural healthcare settings, from prescribing to post-administration monitoring. It identifies four main themes: the types of AI used, the medication phases impacted, the effectiveness in reducing errors, and rural-specific challenges such as infrastructure and alert fatigue. Studies show machine‑learning surveillance can cut prescribing and transcription errors by 34% to 80%, yet barriers like governance gaps, funding limits, and clinician resistance remain.

7 Crucial Barriers Between Data Teams and Self-Healing Data Architecture

What data teams need to build with AI to make self-healing data architecture a practical reality The post 7 Crucial Barriers Between Data Teams and Self-Healing Data Architecture appeared first on Towards Data Science .

"We'll have to see how it works": An interview study to understand collaborative practices in interdisciplinary artificial intelligence and healthcare research

arXiv:2311. 18424v3 Announce Type: replace-cross Abstract: Developing artificial intelligence (AI) algorithms for healthcare is a collaborative effort, bringing data scientists, clinicians, patients and other stakeholders together.

H2: A Dual Hybrid Semantic Data Lake Architecture for Medical Data Harmonization with Human-In-the-Loop verified, LLM Driven Metadata Annotation System

arXiv:2608. 08056v1 Announce Type: new Abstract: Medical data, by its nature, exhibit a high degree of heterogeneity on multiple levels ranging from (a) different modalities like images, text and time series, (b) diverse tabular schemata introduced by institutions and (c) completely unstructured textual information data provided by healthcare professionals.

Improving Access to Essential Medicines via Decision-Aware Machine Learning

arXiv:2607. 20542v1 Announce Type: cross Abstract: A critical challenge in healthcare systems in low- and middle-income countries (LMICs) is the efficient and equitable allocation of scarce resources, particularly essential medicines.