The device is designed for small blood samples collected through minimally invasive methods, including finger pricks. Once a sample is placed inside the testing unit, integrated sensors measure properties of the blood. Those measurements are processed by computational models that generate results and transmit them digitally to patients and healthcare professionals. By moving part of the testing process closer to the patient, the system could eventually support more frequent monitoring in homes, clinics and other community settings.

Traditional blood testing usually produces a snapshot of physiology at a particular moment. That snapshot can be clinically valuable, but it may not reveal subtle changes that emerge between appointments. Algocyte’s approach is intended to build a time series from repeated measurements. Artificial intelligence can compare new results with an individual’s previous readings, potentially helping identify gradual shifts in blood-cell levels or other physiological signals before they become obvious through symptoms or a single conventional test.

This capability could be especially important for people receiving treatments that require regular surveillance. Patients undergoing chemotherapy, for example, may need repeated blood tests to assess how treatment is affecting their blood cells and whether their immune system is being compromised. Similar monitoring is required for people taking medicines such as clozapine, which can cause serious changes in white blood cell counts in a small proportion of patients. A convenient testing system could reduce the burden of repeated hospital visits while allowing clinicians to maintain closer oversight.

The technology combines several technical disciplines. Bioengineering is used to create a compact platform capable of handling a blood sample and measuring its characteristics, while computational biology helps relate those measurements to biological processes. Artificial intelligence algorithms then analyse patterns in the data. Rather than treating every result as an isolated number, the software is intended to interpret measurements in the context of an individual’s previous results, potentially improving the detection of clinically meaningful trends.

However, the system is not designed to replace medical expertise. Blood results can be affected by factors including infection, medication, hydration, recent treatment and the quality of the sample. AI-generated outputs must therefore be evaluated against established laboratory methods and interpreted alongside a patient’s symptoms and clinical history. The technology’s current development programme includes further testing and evaluation in commercial deployment, as well as the collection of scientific evidence needed to determine how reliably it performs in different healthcare environments.

The device has achieved the UKCA regulatory mark, indicating that it meets relevant UK requirements for health, safety and environmental protection. This milestone allows the technology to progress towards wider use, but it does not by itself establish that the system can replace every laboratory test or make autonomous medical decisions. Algocyte is continuing to work through additional regulatory steps and clinical evaluation as it explores applications in routine monitoring and, potentially, predictive healthcare.

The possible impact extends beyond individual patients. Millions of blood tests are carried out in the United Kingdom each year to diagnose disease, monitor treatment and assess general health. Many require appointments, sample transport and laboratory processing, placing demands on clinical staff and healthcare infrastructure. A reliable point-of-care system could help redistribute some of this workload, allowing healthcare professionals to focus laboratory resources on complex analyses while routine measurements are collected closer to the patient.

The PROXIMA™ Mobile Health Station was officially launched at the Royal Society of Medicine in London on 29 June, following the achievement of its UKCA marking. The event brought together researchers, clinicians, industry partners and healthcare leaders to discuss the device’s future development, clinical validation and potential applications. Hector Zenil, Algocyte’s founder and chief executive and an associate professor in healthcare engineering at King’s College London, described the project as an example of how academic research in artificial intelligence, cell biology and medicine can be translated into technologies with potential social impact. If further testing confirms its accuracy and clinical value, the system could help transform blood monitoring from an episodic hospital procedure into a more continuous and personalised part of healthcare.

Subject of Research: Portable AI-enabled blood testing and remote health monitoring

Article Title: A Pocket-Sized AI Blood Analyzer Could Bring Continuous Health Monitoring Into the Home

References: King’s College London and Algocyte announcement concerning the PROXIMA™ Mobile Health Station and its UKCA marking

Keywords

Portable blood testing, artificial intelligence, home healthcare, remote monitoring, point-of-care diagnostics, chemotherapy monitoring, clozapine monitoring, bioengineering, digital health, King’s College London, Algocyte, PROXIMA Mobile Health Station

The core challenge is that a single clinical label can conceal multiple underlying mechanisms. Two patients labeled with the same condition may differ dramatically in how their disease unfolds, how risk accumulates, and how they respond to treatment. Until now, researchers have lacked a practical framework to connect diseases over years and across individuals in a single coherent model.

The study addresses three linked questions: how seemingly unrelated diseases relate across time, what joint modeling of hundreds of conditions can reveal about hidden biology, and how genetic variation shapes individual disease “signatures” and progression paths. The goal is not only prediction, but interpretability—capturing the latent processes that drive real-world trajectories.

To do this, the team developed ALADYNOULLI, an advanced generative Bayesian model. It learns latent disease signatures by integrating longitudinal diagnosis patterns with age and genetic risk information. By borrowing statistical strength across patients and conditions, the model discovers shared mechanisms that may remain invisible when diseases are analyzed separately.

In extensive evaluations, ALADYNOULLI compressed 348 diseases into 21 reproducible latent signatures. These signatures aligned closely across three independent biobanks totaling more than 683,000 individuals with up to 52 years of follow-up, suggesting the learned processes are stable rather than dataset-specific.

Genetic analyses tied to the signatures confirmed established associations and surfaced additional signals that would likely be missed by single-disease approaches. In other words, the “biological fingerprint” of a patient’s disease is distributed across a pattern of latent processes—not trapped within a diagnostic label.

The model also demonstrated strong ability to forecast future disease risk. Crucially, risk estimates improved when predictions were updated as new EHR information arrived, mirroring how clinical care unfolds over time rather than at a single snapshot.

Beyond performance, the most consequential implication may be practical transferability. Because ALADYNOULLI can generalize across health systems without requiring every site to have access to an enormous genetic dataset, it could support wider deployment of dynamic, evolving risk assessment.

Ultimately, the work aims to make disease trajectories multidimensional and concrete. Two patients with the same diagnosis can map onto distinct latent signature profiles—capturing why their progression and treatment responses diverge.

Subject of Research: Bayesian generative modeling of longitudinal EHR and genetics to discover latent disease signatures and improve dynamic disease prediction.
Article Title: A Bayesian framework for longitudinal HER and genetic discovery
News Publication Date: 15-Jul-2026
Web References: https://www.nature.com/articles/s41586-026-10780-5 ; http://dx.doi.org/10.1038/s41586-026-10780-5
References: Urbut, S., et al. “A Bayesian framework for longitudinal HER and genetic discovery.” Nature. DOI: 10.1038/s41586-026-10780-5
Image Credits: Not provided
Keywords: Human genetics, electronic health records, Bayesian models, longitudinal prediction, latent disease signatures

Traditionally, cardiometabolic risk prediction algorithms have been developed with the general population in mind, often targeting middle-aged or older adults. This approach has inherently neglected the unique physiological and lifestyle factors prevalent in younger cohorts, especially those grappling with psychosis. PsyMetRiC 2.0 bridges this divide by utilizing a refined algorithm, honed through the rigorous analysis of anonymized health data from over 25,000 young people with psychosis in the United Kingdom, whose clinical trajectories were tracked longitudinally over two decades.

The methodology employed is a landmark in predictive modeling: by harnessing real-world electronic health records, researchers created a model capable of predicting three critical outcomes. Within one year, it estimates significant weight gain; over six years, the onset of metabolic syndrome; and within ten years, the development of type 2 diabetes. These outcomes were chosen not merely for their clinical relevance but also for their resonance with patient priorities, ensuring the tool’s recommendations are grounded in shared decision-making principles.

What differentiates PsyMetRiC’s approach is its conscientious design for utility and fairness. It was rigorously validated across multiple international cohorts, including populations in Spain, Switzerland, Finland, the Netherlands, Canada, Hong Kong, and Australia, demonstrating robust predictive performance beyond the UK. Furthermore, the designers incorporated feedback from clinicians, carers, and those with lived experience of psychosis, in partnership with organizations such as the McPin Foundation and The Centre for Mental Health. This collaborative process ensured that the tool not only delivers precise risk assessments but also communicates these risks in a manner that is accessible, culturally sensitive, and motivating for patients.

At the core of PsyMetRiC 2.0’s architecture is advanced statistical analysis and machine learning techniques applied to large-scale, longitudinal datasets. By identifying complex interactions between demographic factors, clinical presentations, medication regimens—particularly antipsychotic-induced metabolic side effects—and lifestyle parameters like diet, exercise, and smoking, the algorithm provides personalized risk profiles. The predictive models incorporate both fixed and dynamic variables, accounting for changes in health status over time, which enhances their clinical relevance in monitoring disease progression and guiding timely interventions.

A significant achievement of PsyMetRiC is its certification by the UK Medicines & Healthcare products Regulatory Agency (MHRA) as a Class 1 Medical Device. This regulatory endorsement is historic within psychiatry, underscoring the tool’s safety, efficacy, and readiness for integration into routine clinical workflows. Its deployment offers a paradigm shift, encouraging clinicians to move beyond reactive care and towards proactive, prevention-oriented strategies tailored to the complex needs of young people with severe mental illness.

The clinical implications of deploying PsyMetRiC extend beyond individual patient outcomes. People living with psychosis experience substantially reduced life expectancy, averaging a 15-year gap compared to the general population, predominantly due to preventable cardiometabolic diseases. Early identification of risk allows for the initiation of lifestyle modifications and pharmacological treatments—such as metformin or statins—aimed at mitigating weight gain and metabolic disturbances. The availability of a quantifiable risk score also facilitates nuanced conversations between healthcare providers and patients, helping dismantle barriers related to health literacy and stigma.

Emphasizing patient engagement, PsyMetRiC’s risk reports are multifaceted, incorporating numeric probabilities alongside graphical visualizations, ranging from traditional risk charts to innovative ‘heart age’ analogues. This multimodal communication strategy caters to diverse patient preferences and cognitive styles, enhancing comprehension and fostering behavior change. Importantly, educational materials co-produced with individuals with lived experience accompany the application, guiding clinicians on optimal risk discussion techniques to maximize impact.

The research underpinning PsyMetRiC 2.0 is published in the highly regarded journal The Lancet Psychiatry, signaling its scientific rigor and clinical significance. The study employed retrospective multicohort analysis with sophisticated data/statistical methods, ensuring that the model’s validations are both methodologically sound and clinically applicable. Planned future directions include refining the algorithm using results from ongoing qualitative and health economic evaluations, as well as expanding its validation in non-UK populations, including forthcoming trials in the United States.

The developers recognize that health inequities are embedded within many datasets, potentially propagating bias in predictive models. By actively seeking to test and correct for such biases, PsyMetRiC represents an important step toward equitable healthcare delivery. The tool aims to serve patients from diverse ethnic and socioeconomic backgrounds, addressing disparities that have historically marginalized these groups in physical health management.

In summary, PsyMetRiC 2.0 embodies a convergence of advanced analytics, patient-centered design, and regulatory validation, poised to revolutionize the management of cardiometabolic risk in young people with psychosis. Its introduction marks a pivotal moment in psychiatric medicine, promising to reduce premature mortality through early, personalized intervention. As this tool gains traction in clinical settings, it holds the potential to reshape how mental and physical health intersect in vulnerable populations globally.

Subject of Research: People
Article Title: Cardiometabolic prediction models for young people with psychosis spectrum disorders in the UK (PsyMetRiC 2.0): a retrospective, multicohort clinical prediction model study
News Publication Date: 11-Mar-2026
Web References:

• PsyMetRiC Web Application: https://psymetric.app/
• Lancet Psychiatry Article: https://www.thelancet.com/journals/lanpsy/article/PIIS2215-0366(25)00398-0/fulltext
  References:
• Perry, B. et al., “Cardiometabolic prediction models for young people with psychosis spectrum disorders in the UK (PsyMetRiC 2.0),” The Lancet Psychiatry, 2026.
• Original PsyMetRiC Validation Study: https://pmc.ncbi.nlm.nih.gov/articles/PMC8211566/
  Keywords: Psychotic disorders, Cardiometabolic risk, Metabolic syndrome, Type 2 diabetes, Obesity, Machine learning, Health equity, Psychiatry, Predictive modeling