Undiagnosed and untreated human immunodeficiency virus (HIV) infection increases morbidity in the HIV-positive person and allows onward transmission of the virus. Minimizing missed opportunities for HIV diagnosis when a patient visits a healthcare facility is essential in restraining the epidemic and working toward its eventual elimination. Most state-of-the-art proposals employ machine learning (ML) methods and structured data to enhance HIV diagnoses, however, there is a dearth of recent proposals utilizing unstructured textual data from Electronic Health Records (EHRs). In this work, we propose to use only the unstructured text of the clinical notes as evidence for the classification of patients as suspected or not suspected. For this purpose, we first compile a dataset of real clinical notes from a hospital with patients classified as suspects and non-suspects of having HIV. Then, we evaluate the effectiveness of two types of classification models to identify patients suspected of being infected with the virus: classical ML algorithms and two Large Language Models (LLMs) from the biomedical domain in Spanish. The results show that both LLMs outperform classical ML algorithms in the two settings we explore: one dataset version is balanced, containing an equal number of suspicious and non-suspicious patients, while the other reflects the real distribution of patients in the hospital, being unbalanced. We obtain F1 score figures of 94.7 with both LLMs in the unbalanced setting, while in the balance one, RoBERTaBio model outperforms the other one with a F1 score of 95.7. The findings indicate that leveraging unstructured text with LLMs in the biomedical domain yields promising outcomes in diminishing missed opportunities for HIV diagnosis. A tool based on our system could assist a doctor in deciding whether a patient in consultation should undergo a serological test.

In recent years, there has been a significant improvement in the accuracy of the classification of pigmented skin lesions using artificial intelligence algorithms. Intelligent analysis and classification systems are significantly superior to visual diagnostic methods used by dermatologists and oncologists. However, the application of such systems in clinical practice is severely limited due to a lack of generalizability and risks of potential misclassification. Successful implementation of artificial intelligence-based tools into clinicopathological practice requires a comprehensive study of the effectiveness and performance of existing models, as well as further promising areas for potential research development. The purpose of this systematic review is to investigate and evaluate the accuracy of artificial intelligence technologies for detecting malignant forms of pigmented skin lesions. For the study, 10,589 scientific research and review articles were selected from electronic scientific publishers, of which 171 articles were included in the presented systematic review. All selected scientific articles are distributed according to the proposed neural network algorithms from machine learning to multimodal intelligent architectures and are described in the corresponding sections of the manuscript. This research aims to explore automated skin cancer recognition systems, from simple machine learning algorithms to multimodal ensemble systems based on advanced encoder-decoder models, visual transformers (ViT), and generative and spiking neural networks. In addition, as a result of the analysis, future directions of research, prospects, and potential for further development of automated neural network systems for classifying pigmented skin lesions are discussed.

Diabetic retinopathy (DR) is a vision-threatening complication of diabetes, necessitating early and accurate diagnosis. The combination of optical coherence tomography (OCT) imaging with convolutional neural networks (CNNs) has emerged as a promising approach for enhancing DR diagnosis. OCT provides detailed retinal morphology information, while CNNs analyze OCT images for automated detection and classification of DR. This paper reviews the current research on OCT imaging and CNNs for DR diagnosis, discussing their technical aspects and suitability. It explores CNN applications in detecting lesions, segmenting microaneurysms, and assessing disease severity, showing high sensitivity and accuracy. CNN models outperform traditional methods and rival expert ophthalmologists\' results. However, challenges such as dataset availability and model interpretability remain. Future directions include multimodal imaging integration and real-time, point-of-care CNN systems for DR screening. The integration of OCT imaging with CNNs has transformative potential in DR diagnosis, facilitating early intervention, personalized treatments, and improved patient outcomes. Abbreviations: DR = Diabetic Retinopathy, OCT = Optical Coherence Tomography, CNN = Convolutional Neural Network, CMV = Cytomegalovirus, PDR = Proliferative Diabetic Retinopathy, AMD = Age-Related Macular Degeneration, VEGF = vascular endothelial growth factor, RAP = Retinal Angiomatous Proliferation, OCTA = OCT Angiography, AI = Artificial Intelligence.

The development of liquid chromatography UV and mass spectrometry (LC-UV-MS) assays in pharmaceutical analysis is pivotal to improve quality control by providing critical information about drug purity, stability, and presence and identity of byproducts and impurities. Analytical method development of these assays is time-consuming, which often causes it to become a bottle neck in drug development and poses a challenge for process chemists to quickly improve the chemistry. In this study, a systematic and efficient workflow was designed to develop purity assay and purification methods for a wide range of compounds including peptides, proteins, and small molecules with MS-compatible mobile phases (MP) by using automated LC screening instrumentation and in silico modeling tools. Initial LC MPs and chromatography column screening experiments enabled quick identification of conditions which provided the best resolution in the vicinity of the target compounds, which is further optimized using computer-assisted modeling (LC Simulator from ACD/Labs). The experimental retention times were in good agreement with the predicted retention times from LC Simulator (ΔtR < 7%). This workflow presents a practical workflow to significantly expedite the time needed to develop optimized LC-UV-MS methods, allowing for a facile, automatic method optimization and reducing the amount of manual work involved in developing new methods during drug development.

Monoamine transporters are of great interest for their role in the physiological activity of the body and their link to mental and behavioural disorders. Currently, static well-plate assays or manual perfusion systems are used to characterize the interaction of psychostimulants, antidepressants and drugs of abuse with the transporters but still suffer from significant drawbacks caused by lack of automation, for example, low reproducibility, non-comparability of results. An automated microfluidic platform was developed to address the need for more standardized procedures for cell-based assays. An automated system was used to control and drive the simultaneous perfusion of 12 channels on a microfluidic chip, establishing a more standardized protocol to perform release assays to study monoamine transporter-mediated substrate efflux. D-Amphetamine, GBR12909 (norepinephrine transporter) and p-chloroamphetamine, paroxetine (serotonin transporter) were used as control compounds to validate the system. The platform was able to produce the expected releasing (D-Amphetamine, p-chloroamphetamine) or inhibiting (GBR12909, paroxetine) profiles for the two transporters. The reduction of manual operation and introduction of automated flow control enabled the implementation of stronger standardized protocols and the possibility of obtaining higher throughput by increasing parallelization.

BACKGROUND: Emergency department (ED) providers are important collaborators in preventing falls for older adults because they are often the first health care providers to see a patient after a fall and because at-home falls are often preceded by previous ED visits. Previous work has shown that ED referrals to falls interventions can reduce the risk of an at-home fall by 38%. Screening patients at risk for a fall can be time-consuming and difficult to implement in the ED setting. Machine learning (ML) and clinical decision support (CDS) offer the potential of automating the screening process. However, it remains unclear whether automation of screening and referrals can reduce the risk of future falls among older patients.
OBJECTIVE: The goal of this paper is to describe a research protocol for evaluating the effectiveness of an automated screening and referral intervention. These findings will inform ongoing discussions about the use of ML and artificial intelligence to augment medical decision-making.
METHODS: To assess the effectiveness of our program for patients receiving the falls risk intervention, our primary analysis will be to obtain referral completion rates at 3 different EDs. We will use a quasi-experimental design known as a sharp regression discontinuity with regard to intent-to-treat, since the intervention is administered to patients whose risk score falls above a threshold. A conditional logistic regression model will be built to describe 6-month fall risk at each site as a function of the intervention, patient demographics, and risk score. The odds ratio of a return visit for a fall and the 95% CI will be estimated by comparing those identified as high risk by the ML-based CDS (ML-CDS) and those who were not but had a similar risk profile.
RESULTS: The ML-CDS tool under study has been implemented at 2 of the 3 EDs in our study. As of April 2023, a total of 1326 patient encounters have been flagged for providers, and 339 unique patients have been referred to the mobility and falls clinic. To date, 15% (45/339) of patients have scheduled an appointment with the clinic.
CONCLUSIONS: This study seeks to quantify the impact of an ML-CDS intervention on patient behavior and outcomes. Our end-to-end data set allows for a more meaningful analysis of patient outcomes than other studies focused on interim outcomes, and our multisite implementation plan will demonstrate applicability to a broad population and the possibility to adapt the intervention to other EDs and achieve similar results. Our statistical methodology, regression discontinuity design, allows for causal inference from observational data and a staggered implementation strategy allows for the identification of secular trends that could affect causal associations and allow mitigation as necessary.
BACKGROUND: ClinicalTrials.gov NCT05810064; https://www.clinicaltrials.gov/study/NCT05810064.
UNASSIGNED: DERR1-10.2196/48128.

Older adults with multimorbidities have the highest rate of emergency department (ED) usage. These patients are typically on numerous medications, may have underlying dementia, and often present with falls and delirium. Identifying these high-risk older adults for possible intervention is challenging in the ED setting since available screening methods are manual and resource-intensive. The objective is to study the electronic medical record (EMR) use for identifying high-risk older adults in ED. This feasibility study is conducted in an academic ED with 67 000 total and 24% geriatric (age ≥ 65 years) annual visits, American College of Emergency Physician (ACEP) accredited Level 1 Geriatric Emergency Department with an ED-based geriatric consultation program by incorporating criteria from existing manual geriatric screening instruments and the 4M framework into an automated EMR screen to identify high-risk geriatric patients. ED providers are then alerted by an EMR Best Practice Alert (BPA) if high-risk status is identified. Initial development and impact on geriatric ED consults are reported. During the study period, 7450 patient encounters occurred; 1836 (24.6%) encounters involved patients who were 65 years or older. A total of 1398 (76.1%) high-risk ED encounters resulted in BPA alerts using the EMR automated screen. BPA alerts resulted in 82 (5.9%) geriatric evaluations. We conclude that using the EMR to automate screening for older adults for high-risk geriatric conditions in the ED is feasible. An automated EMR screen with a BPA to ED providers identified a well-defined cohort of older patients appropriate for further ED geriatric evaluation.

Contrary to current recommendations, palliative co-management of tumor patients often occurs late in daily clinical practice. Palliative care specialist (PCS) co-management should be considered at the latest after a 6-month prognosis has been presumed. Therefore, identifying patients with a limited prognosis is a reasonable measure.
Patients were identified using a screening tool for limited prognosis, which combined their tumor stage and data from the nursing anamnesis. In this retrospective study, a monocentric cohort of patients with urological malignancies-UICC (Union for International Cancer Control) stages III and IV - were enrolled from March to December 2019, with a 6-month follow-up period ending in May 2020.
Most patients were male and suffered from prostate cancer. Patients with uro-oncological tumors dying within 6 months correlated significantly with the presence of repeated hospitalizations within three months, pain on admission, malnutrition, impaired breathing and reduced mobility (P < 0.001). The test was fair in quality (AUC 0.727) at a cut-point of five; a sensitivity of 97% and a specificity of 25% were obtained. The PPV was 0.64 and NPV was 0.82.
We specifically identified the predictors of limited prognosis in urological cancer patients across several entities using an automated scoring system based on tumor stage and data from the nursing anamnesis. Therefore, we recognized hospitalization as an important transition point and determined nurses to be valuable partners in identifying unmet palliative care needs without additional technical, personnel or financial effort.

BACKGROUND: Microscopic examination is commonly used for malaria diagnosis in the field. However, the lack of well-trained microscopists in malaria-endemic areas impacted the most by the disease is a severe problem. Besides, the examination process is time-consuming and prone to human error. Automated diagnostic systems based on machine learning offer great potential to overcome these problems. This study aims to evaluate Malaria Screener, a smartphone-based application for malaria diagnosis.
METHODS: A total of 190 patients were recruited at two sites in rural areas near Khartoum, Sudan. The Malaria Screener mobile application was deployed to screen Giemsa-stained blood smears. Both expert microscopy and nested PCR were performed to use as reference standards. First, Malaria Screener was evaluated using the two reference standards. Then, during post-study experiments, the evaluation was repeated for a newly developed algorithm, PlasmodiumVF-Net.
RESULTS: Malaria Screener reached 74.1% (95% CI 63.5-83.0) accuracy in detecting Plasmodium falciparum malaria using expert microscopy as the reference after a threshold calibration. It reached 71.8% (95% CI 61.0-81.0) accuracy when compared with PCR. The achieved accuracies meet the WHO Level 3 requirement for parasite detection. The processing time for each smear varies from 5 to 15 min, depending on the concentration of white blood cells (WBCs). In the post-study experiment, Malaria Screener reached 91.8% (95% CI 83.8-96.6) accuracy when patient-level results were calculated with a different method. This accuracy meets the WHO Level 1 requirement for parasite detection. In addition, PlasmodiumVF-Net, a newly developed algorithm, reached 83.1% (95% CI 77.0-88.1) accuracy when compared with expert microscopy and 81.0% (95% CI 74.6-86.3) accuracy when compared with PCR, reaching the WHO Level 2 requirement for detecting both Plasmodium falciparum and Plasmodium vivax malaria, without using the testing sites data for training or calibration. Results reported for both Malaria Screener and PlasmodiumVF-Net used thick smears for diagnosis. In this paper, both systems were not assessed in species identification and parasite counting, which are still under development.
CONCLUSIONS: Malaria Screener showed the potential to be deployed in resource-limited areas to facilitate routine malaria screening. It is the first smartphone-based system for malaria diagnosis evaluated on the patient-level in a natural field environment. Thus, the results in the field reported here can serve as a reference for future studies.

Late HIV diagnosis is associated with increased morbidity, mortality and risk of onward transmission. Increasing HIV early diagnosis is still a priority. In this observational study with historical control, we determined the impact of an opportunistic HIV screening strategy in the reduction of late diagnosis and missed opportunities for earlier diagnosis.
The screening programme was implemented in the emergency department (ED) of the Hospital de Cascais between September 2018 and September 2021. Eligible patients were aged 18-64 years, with no known HIV diagnosis or antibody testing performed in the previous year, and who required blood work for any reason. Out of the 252 153 emergency visits to the ED, we identified 43 153 (17.1%) patients eligible for HIV testing. Among the total population eligible for the screening, 38 357 (88.9%) patients were ultimately tested for HIV. Impact of the ED screening was determined by analysing late diagnosis in the ED and missed opportunities at different healthcare settings 3 years before and 3 years after the start of the ED screening.
After 3 years of automated HIV ED testing, we found 69 newly diagnosed HIV cases (54% male, 39% Portuguese nationals, mean age 40.5 years). When comparing the characteristics of HIV diagnoses made in the ED, we observed a significant reduction in the number of people with late HIV diagnosis before and after implementation of the screening programme (78.4% vs. 39.1%, respectively; p = 0.0291). The mean number of missed opportunities for diagnosis also fell (2.6 vs. 1.5 annual encounters with the healthcare system per patient, p = 0.0997).
People living with HIV in Cascais and their providers miss several opportunities for earlier diagnosis. Opportunistic screening strategies in settings previously deemed to be unconventional, such as EDs, are feasible and effective in mitigating missed opportunities for timely HIV diagnosis.