AI in healthcare Insights: 27th November - 3rd December' 2025

 


Quantum algorithm predicts kidney disease decades earlier than today.

Researchers at Florida Atlantic University have demonstrated a breakthrough in detecting Chronic Kidney Disease (CKD) using a hybrid approach of quantum computing and machine learning. 

Comparing Quantum Support Vector Machines (QSVM) against classical models, the study showed that while classical models currently exhibit higher raw accuracy, quantum algorithms effectively handle the high-dimensional feature spaces of biological data. Using optimization techniques like Principal Component Analysis (PCA), the quantum model successfully identified subtle, non-linear biomarkers. 

This suggests that as quantum hardware matures, hybrid systems could predict kidney decline years before current tests, shifting nephrology from reactive management to proactive prevention by identifying disease trajectories earlier than ever before.

Read the original article at https://medicalxpress.com/news/2025-11-quantum-kidney-disease.html

Mobile health app improves outcomes in chronic disease management.

This systematic review examines the impact of remote patient monitoring (RPM) on healthcare resource utilization in patients with noncommunicable diseases (NCDs). The review included 40 randomized controlled trials (RCTs) from 2017 to 2024. 

Key outcomes included hospitalizations, hospital length of stay, outpatient visits, and emergency visits. The findings suggest that RPM may slightly reduce the proportion of patients hospitalized and the number of hospitalizations compared to usual care, with a small reduction in hospital length of stay. However, the impact on outpatient visits was uncertain, with some studies showing more visits under RPM. The effect on emergency visits was inconsistent and of very low certainty. Overall, RPM might lead to fewer hospitalizations and shorter stays but could result in more outpatient visits, with moderate to very low certainty in the evidence. 

The review calls for cautious interpretation of these results, noting variability in outcomes across different NCDs and the low certainty of some findings. Further research is needed to assess the cost-effectiveness of RPM and explore its application in different healthcare settings and patient groups.

Read the original article at https://mhealth.jmir.org/2025/1/e68464

AI models analysed large-scale health data to personalise treatment.

This study introduces ETHOS, a foundation AI model that treats electronic health records (EHRs) as a language to predict patient outcomes. By "tokenizing" patient histories—converting sequences of diagnoses, medications, and lab results into data streams—ETHOS learns to forecast future medical events using transformer architecture, the same technology behind Large Language Models.

Building on this core, the researchers developed the Adaptive Risk Estimation System (ARES) to generate real-time, personalized risk assessments. Tested on the massive MIMIC-IV dataset, the model significantly outperformed traditional clinical benchmarks in predicting critical outcomes like mortality, ICU admissions, and prolonged hospital stays. Unlike static scoring tools, ARES adapts dynamically as new data enters the record. Crucially, it includes an explainability module that highlights exactly which factors—such as a specific lab spike or medication change—drove the risk prediction. This demonstrates how generative AI can transform raw, unstructured medical data into actionable, personalized clinical intelligence.

Read the original article at https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giaf107/8268904

Wearable sensors + AI detect early signs of neurological disease.

This research proposes a comprehensive framework for the early prediction of neurodegenerative diseases (such as Parkinson’s and ALS) by combining wearable technology with Artificial Intelligence. Addressing the critical gap in diagnosing these conditions during their "prodromal" (pre-symptomatic) phase, the system utilizes a mobile application linked to wearable sensors that continuously monitor vital signs and physiological data.

The core innovation is the use of Recurrent Neural Networks (RNNs) and transfer learning techniques to analyze these longitudinal data streams. Unlike traditional "snapshot" clinical exams, this system detects subtle, non-linear deviations in patient data—such as micro-changes in gait or sleep patterns—that serve as early warning signs of neural decline. The framework also integrates secure authentication (Aadhaar-based) to ensure data privacy while enabling remote monitoring. Preliminary testing with simulated patient data verified the workflow's functionality, suggesting that such a system could democratize access to early diagnosis, particularly in underserved communities where access to neurologists is limited.

Read original article at https://www.researchgate.net/publication/395091085_Smart_Healthcare_Harnessing_AI_for_Early_prediction_of_Neurodegenerative_disease


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