explainable ai

AI-Driven Early Detection of Polycystic Ovary Syndrome via Follicle Count

This study develops machine learning models for early Polycystic Ovary Syndrome prediction using a structured clinical dataset with 42 features and 542 patient records and demonstrates that ensemble models can effectively capture non-linear feature interactions in clinical data.

Probabilistic Programming for Explainable AI – Causal Chain Analysis

Explainable AI (XAI) has emerged as a critical area of research, driven by the need for trust and accountability in increasingly complex AI systems. However, many existing XAI techniques offer only superficial explanations, often failing to capture the underlying causal mechanisms driving AI decisions. This paper propo...

AiWebArmor: A Robust, Precise AI-based Framework for Web Application Firewalls

Artificial intelligence (imaging analysis) fundamentals

Why Enterprise AI Projects Fail: Platform-First Thinking

A $2.3M AI platform launch resulted in zero adoption after 90 days. David Ohnstad explains the critical mistake: building infrastructure before understanding what problems it actually solves. The real roadmap starts with user demand, not infrastructure. Full article: https://davidohnstad.net/why-enterprise-ai-projects-...

Explainable AI via Probabilistic Causal Graph Learning

This paper explores a novel approach to developing explainable artificial intelligence (AI) by leveraging probabilistic causal graph learning. The core premise is that truly explainable AI requires the identification and representation of genuine causal relationships within data, translated into a readily interpretable...

Artificial Intelligence in Leukemia Diagnosis: Applications, Datasets, and Barriers to Clinical Translation

Leukemia diagnosis requires the integration of morphology, hematological parameters, immunophenotyping, cytogenetic or molecular findings, and clinical information. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), is increasingly being investigated as an adjunct to these diagnost...

Enterprise AI Without Platform Replacement: 4 Myths Debunked

Most teams believe AI requires tearing out legacy infrastructure. David Ohnstad explains why this myth costs companies weeks of debate and delayed shipping. Your existing SaaS foundation can support enterprise AI—here's how. Full article: https://davidohnstad.net/enterprise-ai-platform-replacement-myths/

AI for HR Policy Communication

One clause, three readings, and the sentence a model must never write A policy is approved once and read hundreds of times, and almost everything that goes wrong with it happens on the second occasion rather than the first. An approved policy fails as a reading problem rather than a drafting problem. The clause is lawf...

Learning Management Systems in Higher Education A Multidimensional Analysis of Technologies, Adoption, and Innovation

Learning Management Systems (LMSs) have become a core part of higher education because they bring course content, communication, assessment, learner support, and academic information into a common digital environment. This survey examines LMS development from traditional classroom practices and computer-assisted learni...

Refining Our Focus by Bridging Theory and Practice for Real-World Impact: An Updated Scope for JMIR Medical Informatics

Abstract To align with contemporary trends and place greater focus on persistent and novel challenges in the field, JMIR Medical Informatics has updated its focus and scope. Each submitted work will be evaluated against a stronger criterion for translational impact. Additionally, submissions on AI methods and applicati...

Understanding Adults’ Acceptance of AI-Enabled Physician Chatbots for Self-Diagnosis: Development and Validation of the Digital Health Value-Acceptance of Technology (DHVAT) Framework