Artificial Intelligence in Healthcare and Education
New papers in artificial intelligence in healthcare and education, the code and data they release, and the open problems their authors admit. Rebuilt every day from OpenAlex and arXiv.
New papers
Future proofing radiology workflows through MR-STAT and Artificial Intelligence: A New Hope
This PhD thesis investigates several interrelated bottlenecks in radiology: long MRI acquisition times, limited quantitative interpretability of conventional MRI, increasing radiologist workload, and challenges associated with emerging technologies such as AI into clinical practice.
Towards Early and Accurate Disease Detection Through Multimodal Predictive Modeling: Fusion of Electronic Health Records, Medical Imaging, And Omics Data Using Interpretable Machine Learning.
In this paper, we present a comprehensive survey of methods in multimodal predictive modeling for early disease detection, illustrate architectural patterns and fusion strategies, discuss challenges (e.g. missing modalities, interpretability, generalizability, bias), and highlight promising…
Precision Social Medicine: Bridging the Gap Between Social Care and Health Systems Integration
Health systems serving children have increasingly invested in social care initiatives, yet these programs frequently demonstrate inconsistent effectiveness and face challenges in sustainability and scalability.
Artificial Intelligence (AI) Integration in Education: Teachers’ Utilization, Perceptions, and Implementation Challenges
This study explored the integration of Artificial Intelligence (AI) tools in chemistry instruction, focusing on teachers’ utilization patterns, perceptions, and the challenges they encounter.
A Bangla-language retrieval-augmented conversational agent for maternal health in a low-resource setting: design and formative evaluation
Abstract Maternal health information in Bangladesh is difficult to access for women with limited literacy and no English, and perinatal mental health remains under-supported.
Beyond Intelligence: Defining and Measuring Wisdom for AI
To make that testable, we propose the HEED model, built from Shivanand’s reading of the Sanātana (Sanatan) texts; research in AI and psychology has reached many of its parts by other routes.
The Measure of Learning: How Independent Schools are Navigating AI Through Policy, Pedagogy, and Innovation
Artificial intelligence has entered school life more quickly than institutions have developed shared expectations for its use.
Performance of AI Chatbots in Adult Reconstruction Education: A Comparative Evaluation Using AAOS ResStudy Questions.
Artificial intelligence (AI) chatbots are increasingly incorporated into orthopedic education, yet their ability to handle complex, clinically oriented questions remains unclear.
Beyond Accuracy: A Critical Review of Evaluation, Clinical Validation, and Safety Frameworks for Agentic AI in Medicine
We propose a five-level evaluation framework spanning model, agent, trajectory, system, and clinical outcome, together with a clinical validation ladder progressing from component testing and simulation to retrospective evaluation, prospective silent deployment, supervised clinical use, bounded…
Architectural Design and Internal Working of Health Talk: An Offline-Capable, Multilingual AI Conversational Agent for Clinical Triage and Healthcare Facility Discovery in Regional India
To resolve this critical dilemma, this paper presents the complete architectural design, algorithmic foundations, and empirical validation of Health Talk AI—a novel, offline-first, multilingual conversational triage agent specifically engineered for Tier-2 regional healthcare ecosystems such as…
Learning to Think With AI: Task-Level Frameworks for Human-AI Collaboration in Education
As generative AI becomes embedded in how students learn, the central question shifts from whether AI belongs in education to how its use can be structured to build rather than erode critical thinking.
Synthetic Clinical Data for Privacy-Preserving Research
Abstract Background Synthetic clinical records and narratives can support research and information-system testing without routine access to identifiable patient files.
AI Agents in Biological Research: A Review of Evaluation and Security
This paper reviews existing benchmarks and evaluation approaches for AI agents in biological and biomedical research.
Artificial Intelligence (AI) in Patient–Healthcare Relationships: Psychosocial Perspective
Artificial intelligence is becoming part of healthcare through tools that help organise medical information, support clinical decisions, answer common questions, and improve communication.
On the Effectiveness of Data Anonymization and Synthetic Data Generation for Privacy-Preserving Machine Learning in Healthcare
Machine learning on tabular medical data is used for clinical analysis and data-driven decision making.
Inventive Intelligent System for Heart Valve Disease Detection: A Literature Review
In this paper, we review AI, ML, DL technologies used for valve disease diagnosis from echo and cardiac scans.
Artificial Intelligence as a Tool for Addressing Real-World Challenges
Artificial intelligence (AI), machine learning (ML), and data science are increasingly proposed as ways to extend scarce expertise, interpret complex data, and improve the timing and targeting of services in low- and middle-income countries and other resource-constrained communities.
Views on AI as an Instrument of Mental Health Care: Qualitative Perspectives of Psychologists from India and Nepal
Background: AI innovation in research, therapy, education and health care has made informed and strategic implementation an inevitable step forward for practitioners, clients, policy makers and AI developers.
Creating a lung ultrasound information score to measure the information content of pediatric point-of-care lung ultrasound reports.
Designing point-of-care lung ultrasound reporting interfaces requires methods for comparison of the information content of the reports generated.
Technical Report: Assessment of AI-tools as 'Software as a medical device'
This document provides a structured framework for defining and documenting the requirements of a mammography imaging software product that may include artificial intelligence-assisted functions.
Released code and data
Beyond Surface Fluency: A Deterministic-First Hybrid Clinical Auditor Exposes LLM-as-a-Judge Blindness and Behavioral Shifts Under Emergency Clinical Pressure
Code, benchmark specifications, and interactive 420-trace explorer: https://github.com/HammamNM/reasoning-debugger
Large language medicine needs a foundation, not only a frontier
It is against this backdrop that we read the lead article by Guni et al. on "large language medicine (1)." We share the authors' optimism but sharpen their central question: not whether generative and multimodal AI will reshape medicine, but how we build the scaffolding that ensures the reshaping happens safely, equitably, and sustainably.Deep learning, generative AI, large language models (LLMs), and connected agentic systems promise both incremental and transformative change across diagnostics, disease management, surveillance, prevention, and drug discovery.
BigpictureQC: multi-stain, multi-species artifact segmentation for whole-slide quality control
The code, inference pipeline and trained model weights are publicly released.
Assessment of LLMs on basic bioinformatics tasks requiring multi-step reasoning and code writing
AVAILABILITY AND IMPLEMENTATION: The complete analysis was implemented in Python; all code is available at https://github.com/SiwoResearch/lllm-bioinformatics.
Radiology Guideline-derived Decision Graph for Efficient Clinical Decision Support
The source code will be available.
AI-Enabled Patient Profiling for Personalized Nursing Care and Health Support: Scoping Review
CLINICALTRIAL OSF Registry mey4u; https://osf.io/mey4u
Right Answer, Missed Warning
Materials and frozen files: https://github.com/jovanpost/right-answer-missed-warning (commit b735798) Pre-registration PDF SHA-256: ea63504e05d3d39083f6c08a252a618e962f81c588b90b25070cab1d92295694
Assessment validity attrition under deep AI involvement in medical education
Open Science Framework. https://doi.org/10.17605/OSF.IO/VFGEQ License: CC0 1.0 Universal.
Health Data Poverty and Biases in Sub-Saharan African Community Health Settings: A Scoping Review Protocol
Registration The protocol is registered with the Open Science Framework (https//osf.io/qdwr4) to ensure methodological transparency and accountability.
AI Agents Could Help Asia Act On Its Longevity Data (Preprint)
Although the data are available, policymakers often lack the capacity to translate them into timely, detailed action.
Open problems authors admit
AI Agents in Biological Research: A Review of Evaluation and Security
Finally, the paper identifies current limitations and outlines future research directions for developing more reliable, secure, and scientifically capable AI agents for biological research.
Artificial Intelligence in Infertility Management: A Narrative Review of Current Evidence
To ensure safe clinical adoption, future work must prioritize multi-center prospective trials, transparency, and standardized frameworks.
Artificial Intelligence in Dental Disease Detection: Diagnostic Performance, Validation Gaps, and Pathways to Clinical Translation
Important limitations include retrospective study designs, insufficient external validation, data leakage, limited dataset diversity, algorithmic bias, and a lack of prospective clinical outcome studies.
Exploring Psychologists' Perceptions on the Integration, Challenges, and Opportunities on the Use of AI Chatbots: A Phenomenological Study
Recommendations include developing clear guidelines and expanding future research on chatbot applications across diverse populations.
Large Language Models for IoT-Based Diabetes Management: An Extensive Analysis of Uses, Difficulties, and Prospects
The research gaps are identified, and the future directions areproposed so that motivating intelligent, scalable and patient-centric diabetes managementsystems are developed.
Evolution of artificial intelligence in education from technology adoption to ethical governance and educator preparedness
The results indicate that responsible AI use is a prominent strand of the field, and give guidelines for policy, institutional leadership, faculty development and future research.
Context-Aware Multimodal Generative AI Framework for Insulin Stacking Risk Assessment and Clinical Decision Support
Future work is suggested in an evaluation protocol.
GenAI use duration, anxiety, depressive symptoms, and sleep-onset difficulty among medical students in Shanxi, China: a cross-sectional survey
Future research should deploy longitudinal design to clarify the long-term associations between GenAI use duration, mental health, and sleep problems.
Artificial Intelligence-Enhanced Telemedicine: Transforming Diagnosis and Personalized Care in Virtual Health Systems
Challenges, including algorithmic biases and data privacy risks, are discussed, along with future research directions aimed at sustaining AI-driven telemedicine enhancements to health systems worldwide.
Drawbacks and Challenges of Artificial Intelligence in the Medical Field
The paper also discusses possible solutions and future research directions for developing safe, reliable, and responsible medical AI.