While companies promise they can help users monitor gut health, experts suggest that smart toilets provide little in the way of benefits to the average person. It was only a matter of time before wellness trackers --already out there trying to optimize our heart rate, sleep, stress, steps, blood sugar, and menstruation--came for perhaps the most private biosignal of all: our poop. The global gut health market is expected to reach nearly $106 billion by 2029, and a handful of companies are betting they can use stool data to provide in-depth health insights, nudge people to change their diets or lifestyles, and advise them to see a doctor if their bathroom habits seem amiss. But gastroenterologists are skeptical that products that rely on AI to analyze images of what's going on in your toilet bowl are worth the price just yet, and data-privacy advocates warn that people can get locked into long-term subscriptions just to access their own health data. Among the companies touting smart toilets are Kohler Health--an offshoot of the eponymous plumbing giant that sells its device for $449 plus $130 for an annual family membership--and Throne, a startup that raised $10 million in July.

Crowding the gates of a major health care conference, protesters called for Palantir to be booted out of the UK's National Health Service over privacy concerns and political grievances. Protesters wearing hospital gowns and wielding signs gathered outside a UK health care conference on Thursday to object to a deal between the country's National Health Service and American software company Palantir . At 8 am local time, the group, around 80 people in total, crowded the entryway to the NHS ConfedExpo in Manchester. They wanted to appeal to NHS leadership to terminate a contract worth up to $440 million over concerns around national security, data privacy, and the company's political affiliations . The contract, which includes access to Palantir's data analytics and artificial intelligence services, is intended to run until 2031 but includes a break clause that permits the government to withdraw the agreement next February.

Google is sunsetting Google Fit by year’s end. While Fitbit remains very much alive, the rebranded Google Health app is your one-stop shop for all things health and fitness.

US's new scramble for Africa is biomedical imperialism Late in February, Zimbabwe pulled out of a proposed $367m United States health funding agreement after objecting to provisions requiring broad American access to sensitive health data. The five-year programme was presented as support for HIV/AIDS, tuberculosis, malaria and epidemic preparedness efforts. However, the terms demanded extensive sharing of national health intelligence, including epidemiological surveillance data and pathogen samples, while offering no binding guarantees that Zimbabwe would receive equitable access to medical technologies developed from them. Harare called the proposal an "unequal exchange", warning that Zimbabwe risked supplying the "raw materials for scientific discovery" while the resulting benefits could remain concentrated in the United States and global pharmaceutical firms. Critics increasingly describe this pattern as biomedical extractivism: a toxic combination of exploitative research practices and colonial thinking that reinforces Western dominance.

Lagrangian approach, which involves adding a regularization term to the loss function.

Generating synthetic tabular data can be challenging, however evaluation of their quality is just as challenging, if not more. This systematic review sheds light on the critical importance of rigorous evaluation of synthetic health data to ensure reliability, relevance, and their appropriate use. Based on screening of 1766 papers and a detailed review of 101 papers we identified key challenges, including lack of consensus on evaluation methods, improper use of evaluation metrics, limited input from domain experts, inadequate reporting of dataset characteristics, and limited reproducibility of results. In response, we provide several guidelines on the generation and evaluation of synthetic data, to allow the community to unlock and fully harness the transformative potential of synthetic data and accelerate innovation.

This study examined the use of machine learning and domain specific enrichment on patient generated health data, in the form of free text meal logs, to classify meals on alignment with different nutritional goals. We used a dataset of over 3000 meal records collected by 114 individuals from a diverse, low income community in a major US city using a mobile app. Registered dietitians provided expert judgement for meal to goal alignment, used as gold standard for evaluation. Using text embeddings, including TFIDF and BERT, and domain specific enrichment information, including ontologies, ingredient parsers, and macronutrient contents as inputs, we evaluated the performance of logistic regression and multilayer perceptron classifiers using accuracy, precision, recall, and F1 score against the gold standard and self assessment. Even without enrichment, ML outperformed self assessments of individuals who logged meals, and the best performing combination of ML classifier with enrichment achieved even higher accuracies. In general, ML classifiers with enrichment of Parsed Ingredients, Food Entities, and Macronutrients information performed well across multiple nutritional goals, but there was variability in the impact of enrichment and classification algorithm on accuracy of classification for different nutritional goals. In conclusion, ML can utilize unstructured free text meal logs and reliably classify whether meals align with specific nutritional goals, exceeding self assessments, especially when incorporating nutrition domain knowledge. Our findings highlight the potential of ML analysis of patient generated health data to support patient centered nutrition guidance in precision healthcare.

Synthetic data generation creates data based on real-world data using generative models. In health applications, generating high-quality data while maintaining fairness for sensitive attributes is essential for equitable outcomes. Existing GAN-based and LLM-based methods focus on counterfactual fairness and are primarily applied in finance and legal domains. Causal fairness provides a more comprehensive evaluation framework by preserving causal structure, but current synthetic data generation methods do not address it in health settings. To fill this gap, we develop the first LLM-augmented synthetic data generation method to enhance causal fairness using real-world tabular health data. Our generated data deviates by less than 10% from real data on causal fairness metrics. When trained on causally fair predictors, synthetic data reduces bias on the sensitive attribute by 70% compared to real data. This work improves access to fair synthetic data, supporting equitable health research and healthcare delivery.

An artificial intelligence model trained on the medical data of 57 million people who have used the National Health Service in England could one day assist doctors in predicting disease or forecast hospitalisation rates, its creators have claimed. However, other researchers say there are still significant privacy and data protection concerns around such large-scale use of health data, while even the AI's architects say they can't guarantee that it won't inadvertently reveal sensitive patient data. The model, called Foresight, was first developed in 2023. That initial version used OpenAI's GPT-3, the large language model (LLM) behind the first version of ChatGPT, and trained on 1.5 million real patient records from two London hospitals. Now, Chris Tomlinson at University College London and his colleagues have scaled up Foresight to create what they say is the world's first "national-scale generative AI model of health data" and the largest of its kind.

In a world where data is the new currency, wearable health devices offer unprecedented insights into daily life, continuously monitoring vital signs and metrics. However, this convenience raises privacy concerns, as these devices collect sensitive data that can be misused or breached. Traditional measures often fail due to real-time data processing needs and limited device power. Users also lack awareness and control over data sharing and usage. We propose a Privacy-Enhancing Technology (PET) framework for wearable devices, integrating federated learning, lightweight cryptographic methods, and selectively deployed blockchain technology. The blockchain acts as a secure ledger triggered only upon data transfer requests, granting users real-time notifications and control. By dismantling data monopolies, this approach returns data sovereignty to individuals. Through real-world applications like secure medical data sharing, privacy-preserving fitness tracking, and continuous health monitoring, our framework reduces privacy risks by up to 70 percent while preserving data utility and performance. This innovation sets a new benchmark for wearable privacy and can scale to broader IoT ecosystems, including smart homes and industry. As data continues to shape our digital landscape, our research underscores the critical need to maintain privacy and user control at the forefront of technological progress.