More than 1,000 people across the UK with suspected dementia are to be offered a blood test for Alzheimer's disease which it is hoped could revolutionise diagnosis of the disease. The blood test can detect biomarkers for rogue proteins which accumulate in the brains of patients with the condition and will be used in addition to pen and paper cognitive tests, which often misdiagnose it in its early stages. Scientists leading the trial at University College London believe the blood test will improve the accuracy of diagnosis from 70% to more than 90% and want to see how that helps patients and clinicians. Patients will be recruited at 20 memory clinics as part of the study, which aims to see how well the test works within the NHS. Alzheimer's disease is the most common form of dementia and is associated with the build-up in the brain of two rogue proteins - amyloid and tau - which can accumulate for up to 20 years before symptoms emerge.

Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. Effective strategies for early detection are therefore of paramount importance. In recent years, machine learning (ML) has demonstrated considerable potential in healthcare by facilitating the detection of various diseases. In this retrospective development and validation study, we developed an ML model based on dynamic ensemble selection (DES) for LC detection. The model leverages standard blood sample analysis and smoking history data from a large population at risk in Denmark. The study includes all patients examined on suspicion of LC in the Region of Southern Denmark from 2009 to 2018. We validated and compared the predictions by the DES model with diagnoses provided by five pulmonologists. Among the 38,944 patients, 9,940 had complete data of which 2,505 (25\%) had LC. The DES model achieved an area under the roc curve of 0.77$\pm$0.01, sensitivity of 76.2\%$\pm$2.4\%, specificity of 63.8\%$\pm$2.3\%, positive predictive value of 41.6\%$\pm$1.2\%, and F\textsubscript{1}-score of 53.8\%$\pm$1.1\%. The DES model outperformed all five pulmonologists, achieving a sensitivity 9\% higher than their average. The model identified smoking status, age, total calcium levels, neutrophil count, and lactate dehydrogenase as the most important factors for the detection of LC. The results highlight the successful application of the ML approach in detecting LC, surpassing pulmonologists' performance. Incorporating clinical and laboratory data in future risk assessment models can improve decision-making and facilitate timely referrals.

Early detection of inflammatory arthritis (IA) is critical to efficient and accurate hospital referral triage for timely treatment and preventing the deterioration of the IA disease course, especially under limited healthcare resources. The manual assessment process is the most common approach in practice for the early detection of IA, but it is extremely labor-intensive and inefficient. A large amount of clinical information needs to be assessed for every referral from General Practice (GP) to the hospitals. Machine learning shows great potential in automating repetitive assessment tasks and providing decision support for the early detection of IA. However, most machine learning-based methods for IA detection rely on blood testing results. But in practice, blood testing data is not always available at the point of referrals, so we need methods to leverage multimodal data such as semi-structured and unstructured data for early detection of IA. In this research, we present fusion and ensemble learning-based methods using multimodal data to assist decision-making in the early detection of IA, and a conformal prediction-based method to quantify the uncertainty of the prediction and detect any unreliable predictions. To the best of our knowledge, our study is the first attempt to utilize multimodal data to support the early detection of IA from GP referrals.

In this study, a Virus vs. Bacteria machine learning model was developed to discern between these infection types using 16 routine blood test results, C-reactive protein levels, biological sex, and age. With a dataset of 44,120 cases from a single medical center, the Virus vs. Bacteria model demonstrated remarkable accuracy of 82.2%, a Brier score of 0.129, and an area under the ROC curve of 0.91, surpassing the performance of traditional CRP decision rule models. The model demonstrates substantially improved accuracy within the CRP range of 10-40 mg/L, an interval in which CRP alone offers limited diagnostic value for distinguishing between bacterial and viral infections. These findings underscore the importance of considering multiple blood parameters for diagnostic decision-making and suggest that the Virus vs. Bacteria model could contribute to the creation of innovative diagnostic tools. Such tools would harness machine learning and relevant biomarkers to support enhanced clinical decision-making in managing infections.

Binah.ai, the number one provider of AI-powered software that enables video-based measurement of physiological health and wellness parameters, announced its revolutionary ability to measure blood count (hemoglobin levels), blood chemistry (hemoglobin A1C) and lipids (cholesterol total) results by simply having users look at a smartphone camera. "Without a doubt, this is a groundbreaking milestone for Binah.ai, and might be the first step towards bloodless blood tests. Our technology demonstrates promising results in measuring this crucial health data, which, alongside the rest of the vital signs we already deliver, is expected to create a shift in health and wellness monitoring," said David Maman, Co-Founder and CEO of Binah.ai. "Leaping from painful and infrequent blood tests, which billions of people can hardly access, to having them available anytime one needs using just a smartphone camera is pretty revolutionary! Video-based blood tests could have a game-changing impact on the healthcare, insurance and wellness industries. Together, we can help bridge the gaps in healthcare and wellness and ensure that no one gets left behind due to a lack of access," added Maman.

A pandemic came with a lot of unknowns. For example, it was being said that those with underlying conditions are more vulnerable to the coronavirus and must be more cautious. However, we heard that the coronavirus affects young people with no known underlying conditions. This shows many unknowns exist. This is where artificial intelligence or AI can enter to help us find some of our answers if we use it correctly. The underlying conditions include almost everything such as heart diseases, hypertension, diabetes, and chronic respiratory diseases.

Physicians taking care of patients with coronavirus disease (COVID-19) have described different changes in routine blood parameters. However, these changes, hinder them from performing COVID-19 diagnosis. We constructed a machine learning predictive model for COVID-19 diagnosis. The model was based and cross-validated on the routine blood tests of 5,333 patients with various bacterial and viral infections, and 160 COVID-19-positive patients. We selected operational ROC point at a sensitivity of 81.9% and specificity of 97.9%. The cross-validated area under the curve (AUC) was 0.97. The five most useful routine blood parameters for COVID19 diagnosis according to the feature importance scoring of the XGBoost algorithm were MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. tSNE visualization showed that the blood parameters of the patients with severe COVID-19 course are more like the parameters of bacterial than viral infection. The reported diagnostic accuracy is at least comparable and probably complementary to RT-PCR and chest CT studies. Patients with fever, cough, myalgia, and other symptoms can now have initial routine blood tests assessed by our diagnostic tool. All patients with a positive COVID-19 prediction would then undergo standard RT-PCR studies to confirm the diagnosis. We believe that our results present a significant contribution to improvements in COVID-19 diagnosis.

A promising new study published in the preprint online journal medRxiv in April 2020 shows the potential of artificial intelligence (AI) for developing a patient classifier that can separate patients likely to be negative for COVID-19 from among a pool of suspected patients visiting an emergency room (ER). This would reduce the rate of spread significantly, by making it possible to immediately separate the patients most likely to be positive from others with similar symptoms of respiratory illness. It would protect both patients and healthcare providers from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The novel coronavirus (SARS-CoV-2) has spread across the world at unprecedented speed, placing a heavy and, in some cases, practically unsustainable, load on healthcare systems. Despite government aid, many healthcare providers find themselves requiring many more beds, intensive care units (ICU), and Personal Protective Equipment (PPE) than can be provided.

The tumour is hard to miss on the scan. The size of a golf ball, it sits bold and white on the brain stem, a part of the organ that sends messages back and forth between body and brain. In many ways it is the master controller: from the top of the spinal cord, the brain stem conducts every heartbeat, every swallow, every breath. For this young man, the cancer came to light in dramatic fashion. The growing tumour blocked fluid draining from his brain, triggering a huge seizure.

The tumour is hard to miss on the scan. The size of a golf ball, it sits bold and white on the brain stem, a part of the organ that sends messages back and forth between body and brain. In many ways it is the master controller: from the top of the spinal cord, the brain stem conducts every heartbeat, every swallow, every breath. For this young man, the cancer came to light in dramatic fashion. The growing tumour blocked fluid draining from his brain, triggering a huge seizure.