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Introduction: Automated machine learning (autoML) removes technical and technological barriers to building artificial intelligence models. We aimed to summarise the clinical applications of autoML, assess the capabilities of utilised platforms, evaluate the quality of the evidence trialling autoML, and gauge the performance of autoML platforms relative to conventionally developed models, as well as each other. Method: This review adhered to a prospectively registered protocol (PROSPERO identifier CRD42022344427). The Cochrane Library, Embase, MEDLINE and Scopus were searched from inception to 11 July 2022. Two researchers screened abstracts and full texts, extracted data and conducted quality assessment. Disagreement was resolved through discussion and as if required, arbitration by a third researcher. Results: There were 26 distinct autoML platforms featured in 82 studies. Brain and lung disease were the most common fields of study of 22 specialties. AutoML exhibited variable performance: area under the receiver operator characteristic curve (AUCROC) 0.35–1.00, F1-score 0.16–0.99, area under the precision-recall curve (AUPRC) 0.51–1.00. AutoML exhibited the highest AUCROC in 75.6% trials; the highest F1-score in 42.3% trials; and the highest AUPRC in 83.3% trials. In autoML platform comparisons, AutoPrognosis and Amazon Rekognition performed strongest with unstructured and structured data, respectively. Quality of reporting was poor, with a median DECIDE-AI score of 14 of 27. Conclusion: A myriad of autoML platforms have been applied in a variety of clinical contexts. The performance of autoML compares well to bespoke computational and clinical benchmarks. Further work is required to improve the quality of validation studies. AutoML may facilitate a transition to data-centric development, and integration with large language models may enable AI to build itself to fulfil user-defined goals.

Large language models (LLMs) underlie remarkable recent advanced in natural language processing, and they are beginning to be applied in clinical contexts. We aimed to evaluate the clinical potential of state-of-the-art LLMs in ophthalmology using a more robust benchmark than raw examination scores. We trialled GPT-3.5 and GPT-4 on 347 ophthalmology questions before GPT-3.5, GPT-4, PaLM 2, LLaMA, expert ophthalmologists, and doctors in training were trialled on a mock examination of 87 questions. Performance was analysed with respect to question subject and type (first order recall and higher order reasoning). Masked ophthalmologists graded the accuracy, relevance, and overall preference of GPT-3.5 and GPT-4 responses to the same questions. The performance of GPT-4 (69%) was superior to GPT-3.5 (48%), LLaMA (32%), and PaLM 2 (56%). GPT-4 compared favourably with expert ophthalmologists (median 76%, range 64-90%), ophthalmology trainees (median 59%, range 57-63%), and unspecialised junior doctors (median 43%, range 41-44%). Low agreement between LLMs and doctors reflected idiosyncratic differences in knowledge and reasoning with overall consistency across subjects and types (p>0.05). All ophthalmologists preferred GPT-4 responses over GPT-3.5 and rated the accuracy and relevance of GPT-4 as higher (p

We ascertained the burden, determinants, and care cascade in the young and middle-aged patients having co-existing hypertension (HTN), Diabetes Mellitus (DM), and abdominal obesity in India from a secondary data analysis of nationally representative data. The study examined cross-sectional data from the National Family Health Survey (NFHS-5) conducted in India from 2019 to 2021 in 788974 individuals aged 15-49 years including 695707 women and 93267 men. The weighted prevalence of DM-HTN comorbidity with high waist circumference in the sample was 0.75% (95% CI: 0.71 to 0.79) including 46.33% (95% CI: 44.06 to 48.62) newly diagnosed cases detected for HTN and high blood sugars. The weighted prevalence of Metabolic syndrome as per NCEP ATPIII criteria was found to be 1.13% (95% CI: 1.08 to 1.17). Only 46.16% existing cases were treated with both anti-diabetes and antihypertensive medication (full treatment), while 34.71% cases were untreated. On adjusted analysis, increasing age, females, higher wealth index, high fat diet, obesity and comorbidities were significantly associated with having DM-HTN comorbidity along with high-waist circumference. More than half of young and middle aged-population in India with DM-HTN-abdominal obesity triad are not initiated on treatment for DM and HTN comorbidities, while a majority of the previously diagnosed cases have uncontrolled blood pressure and poor glycemic control. The poor cascade of care for DM and HTN in these high-risk group of patients may substantially increase their risk for early progression and severity of microvascular and macrovascular complications especially cardiovascular disease.

Introduction Metabolic syndrome is a significance driver of mortality and morbidity in India, but nationally representative data regarding disease burden and treatment status are lacking. Here, a cross-sectional study was undertaken to establish national and regional estimates of disease burden and explore reasons for lack of treatment of component conditions of metabolic syndrome in Indian older adults (45 years and older).Methods A cross-sectional study was undertaken using data from the first wave of the Longitudinal Ageing Study in India (2017–2018). Data for 66 606 individuals aged 45 years and above were analysed. The primary outcome was metabolic syndrome prevalence, defined by the National Cholesterol Education Programme ATP III criteria as an individual having any three of four component conditions: diabetes mellitus (DM), hypertension, abdominal obesity and hypercholesterolaemia. The secondary outcome of this study was treatment status of patients with component conditions.Results Metabolic syndrome was found to have an overall weighted prevalence of 4.83% (n=3630, 95% CI 4.24 to 5.51). Females, urban residents, obese individuals and physically inactive people exhibited greater prevalence. The most prevalent component of metabolic syndrome was hypertension followed by abdominal obesity, DM and hypercholesterolaemia. 8.85% metabolic syndrome patient reported no treatment for component conditions, while 17.58% reported only partial treatment. Elderly individuals between 60 and 69 (crude relative risk ratios, cRRR 2.20, 95% CI 1.20 to 4.01) and 80 years and above (cRRR 7.48, 95% CI 1.99 to 28.16), urban residents (cRRR 2.45, 95% CI 1.48 to 4.05), those from richer monthly per capita consumption expenditure quintiles (cRRR 2.55, 95% CI 1.00 to 6.47) and those with additional comorbidities (cRRR 2.17, 95% CI 1.28 to 3.70) were more likely to report comprehensive treatment.Conclusions This study highlights a substantial prevalence of metabolic syndrome in older adults in India and reveals remarkable disparities in provision of treatment. Better prevention, earlier detection and improved provision of treatment are urgently required to combat the rising prevalence of metabolic syndrome and reduce the burden of cardiovascular disease in India.

Background: Non-communicable diseases (NCDs) are a leading cause of death globally and disproportionately affect those in low- and middle-income countries lower-middle-income countries. Poor medication adherence among patients with NCDs is prevalent in India due to lack of initiation, missed dosing or cessation of treatment, and represents a growing healthcare and financial burden.Objective: This study aimed to identify factors influencing medication adherence in adults with NCDs in India.Methods: We performed a cross-sectional study, conducting secondary data analysis on the second wave of the World Health Organisation’s ‘Study on global AGEing and adult health (SAGE)’, a survey that collected data from predominantly older adults across India. Bivariate analysis and multivariate logistic regression modelling were conducted to specifically interrogate the reasons for lack of initiation and cessation of treatment. Reporting of this study was informed by the STROBE guidelines.Results: The average medication adherence rate was 51% across 2,840 patients with one or more NCDs, reflecting non-initiation and lack of persistence of treatment. The strongest factor significantly predicting non-adherence to medication across these components was multimorbidity (odds ratio 0.47, 95% CI 0.40–0.56). Tobacco use (OR = 0.76, CI 0.59–0.98) and never having attended school (OR = 0.75, CI 0.62–0.92) were significantly associated with poor medication adherence (p < 0.05) while rural living (OR = 0.70, CI 0.48–1.02), feelings of anxiety (OR = 0.84, CI 0.66–1.08) and feelings of depression (OR = 0.90, CI 0.70–1.16) were factors lacking statistically significant association with medication adherence on multivariate analysis. Older age (OR = 2.02, CI 1.51–2.71) was significantly associated with improved medication adherence whilst there was a weak association between increased wealth and improved medication use.Limitations: The SAGE2 survey did not capture whether patients were taking their medication doses according to prescribed instructions—as a result our findings may under-estimate the true prevalence of medication non-adherence.Conclusion: Our analysis provides evidence that poor medication adherence in India is multifactorial, with distinct socioeconomic and health-system factors interacting to influence patient decision making. Future large-scale surveys interrogating adherence should assess all components of adherence specifically, whilst public health interventions to improve medication adherence should focus on barriers that may exist due to multimorbidity, comorbid depression and anxiety, and low educational status.

IntroductionCost-effective interventions that improve medication adherence are urgently needed to address the epidemic of non-communicable diseases (NCDs) in India. However, in low- and middle-income countries like India, there is a lack of analysis evaluating the effectiveness of adherence improving strategies. We conducted the first systematic review evaluating interventions aimed at improving medication adherence for chronic diseases in India.MethodsA systematic search on MEDLINE, Web of Science, Scopus, and Google Scholar was conducted. Based on a PRISMA-compliant, pre-defined methodology, randomized control trials were included which: involved subjects with NCDs; were located in India; used any intervention with the aim of improving medication adherence; and measured adherence as a primary or secondary outcome.ResultsThe search strategy yielded 1,552 unique articles of which 22 met inclusion criteria. Interventions assessed by these studies included education-based interventions (n = 12), combinations of education-based interventions with regular follow up (n = 4), and technology-based interventions (n = 2). Non-communicable diseases evaluated commonly were respiratory disease (n = 3), type 2 diabetes (n = 6), cardiovascular disease (n = 8) and depression (n = 2).ConclusionsAlthough the vast majority of primary studies supporting the conclusions were of mixed methodological quality, patient education by CHWs and pharmacists represent promising interventions to improve medication adherence, with further benefits from regular follow-up. There is need for systematic evaluation of these interventions with high quality RCTs and their implementation as part of wider health policy.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022345636, identifier: CRD42022345636.

BackgroundLarge language models exhibiting human-level performance in specialized tasks are emerging; examples include Generative Pretrained Transformer 3.5, which underlies the processing of ChatGPT. Rigorous trials are required to understand the capabilities of emerging technology, so that innovation can be directed to benefit patients and practitioners. ObjectiveHere, we evaluated the strengths and weaknesses of ChatGPT in primary care using the Membership of the Royal College of General Practitioners Applied Knowledge Test (AKT) as a medium. MethodsAKT questions were sourced from a web-based question bank and 2 AKT practice papers. In total, 674 unique AKT questions were inputted to ChatGPT, with the model’s answers recorded and compared to correct answers provided by the Royal College of General Practitioners. Each question was inputted twice in separate ChatGPT sessions, with answers on repeated trials compared to gauge consistency. Subject difficulty was gauged by referring to examiners’ reports from 2018 to 2022. Novel explanations from ChatGPT—defined as information provided that was not inputted within the question or multiple answer choices—were recorded. Performance was analyzed with respect to subject, difficulty, question source, and novel model outputs to explore ChatGPT’s strengths and weaknesses. ResultsAverage overall performance of ChatGPT was 60.17%, which is below the mean passing mark in the last 2 years (70.42%). Accuracy differed between sources (P=.04 and .06). ChatGPT’s performance varied with subject category (P=.02 and .02), but variation did not correlate with difficulty (Spearman ρ=–0.241 and –0.238; P=.19 and .20). The proclivity of ChatGPT to provide novel explanations did not affect accuracy (P>.99 and .23). ConclusionsLarge language models are approaching human expert–level performance, although further development is required to match the performance of qualified primary care physicians in the AKT. Validated high-performance models may serve as assistants or autonomous clinical tools to ameliorate the general practice workforce crisis.

Background Remote self-administered visual acuity (VA) tests have the potential to allow patients and non-specialists to assess vision without eye health professional input. Validation in pragmatic trials is necessary to demonstrate the accuracy and reliability of tests in relevant settings to justify deployment. Here, published pragmatic trials of these tests were synthesised to summarise the effectiveness of available options and appraise the quality of their supporting evidence. Methods A systematic review was undertaken in accordance with a preregistered protocol (CRD42022385045). The Cochrane Library, Embase, MEDLINE, and Scopus were searched. Screening was conducted according to the following criteria: (1) English language; (2) primary research article; (3) visual acuity test conducted out of eye clinic; (4) no clinical administration of remote test; (5) accuracy or reliability of remote test analysed. There were no restrictions on trial participants. Quality assessment was conducted with QUADAS-2. Results Of 1227 identified reports, 10 studies were ultimately included. One study was at high risk of bias and two studies exhibited concerning features of bias; all studies were applicable. Three trials—of DigiVis, iSight Professional, and Peek Acuity—from two studies suggested that accuracy of the remote tests is comparable to clinical assessment. All other trials exhibited inferior accuracy, including conflicting results from a pooled study of iSight Professional and Peek Acuity. Two studies evaluated test-retest agreement—one trial provided evidence that DigiVis is as reliable as clinical assessment. The three most accurate tests required access to digital devices. Reporting was inconsistent and often incomplete, particularly with regards to describing methods and conducting statistical analysis. Conclusions Remote self-administered VA tests appear promising, but further pragmatic trials are indicated to justify deployment in carefully defined contexts to facilitate patient or non-specialist led assessment. Deployment could augment teleophthalmology, non-specialist eye assessment, pre-consultation triage, and autonomous long-term monitoring of vision.

The ability to develop a work environment climate supporting technological innovation does not occur suddenly or unexpectedly. The objectives of this paper is to assess the technological innovation climate in one of the largest oil and gas company in the world, to compare the results with published standards, and to suggest recommendations to improve the innovation climate in the company. The study used the situational outlook questionnaire to assess nine key dimensions of innovation climate. The results showed that the extent to which each of the nine dimensions is applied were: debate (70%); idea support (67%); challenge/involvement (67%); trust and openness (59%); freedom (57%); idea time (56%); playfulness/humor (52%); risk taking (50%) and conflict (46%). These results were compared with published norms and the results revealed that the company is categorized as innovative in debate, idea time, idea support and trust; categorized as average in challenge and risk taking; and as stagnated in freedom, playfulness and conflict. The comparison using the t-test confirmed the results. Recommendations and details of the actions required for improvement were outlined.

Mapping technological innovation in organizations is one of the important activities that help companies to identify where organizations are clustering their innovation efforts, and where their unexplored innovation spaces are. Current published innovation mapping models do not take into consideration the comparison and benchmarking between organizations in one model. The objectives of this paper are to map innovation in three international petrochemical companies: Gulf Petrochemical Industries Company (GPIC), Saudi Basic Industries Corporation (SABIC), and Dow Chemical; compare and benchmark the results; and explore the possible areas for their innovation opportunities. An innovation mapping model was developed. Innovation data covering three years (2010-2012), were collected, analyzed and mapped on the model. The results showed that the three companies introduced a total of 194 innovations; 53% by Dow Chemical, 38% by SABIC and 9% by GPIC. Product innovations were the dominant type as they presented 57% of total innovations, where 54% of these were introduced by Dow Chemical, 46% by SABIC, and none by GPIC. Position and paradigm innovations were the least innovation type produced, where only 3% of the total innovations were in position and 1% in paradigm. The results also showed that multi-dimensional innovation represented 23.7% of total innovations, where 67.5% of these were produced by Dow Chemical, 28% by SABIC, and only 4.5% by GPIC. Product-process innovations represented 50% of the total multi-dimensional innovations. During this period only 5.7% of the total innovations were radical innovations; these were all introduced by Dow Chemical. The benchmarking results showed that product innovation was the strength in SABIC; process innovation was the strength in GPIC; and product, radical, product-position, process-position and product-paradigm were the strengths in Dow Chemical. For GPIC there are possible innovation opportunities in product, product-process and process-position innovations; for Dow Chemical and SABIC, in the process area. There are possible opportunities in radical innovation in GPIC and SABIC and plenty of innovation opportunities in the position and paradigm areas for the three companies.