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Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations.


Machine Learning And The Future Of Healthcare

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Artificial Intelligence has been around for quite some time and its potential to transform our lives has been frequently debated. Powered by the growth of abilities in computational hardware and related algorithm development, AI research programs have ebbed and flowed. The use of AI in healthcare in India is steadily increasing with new startups and large ICT companies providing AI solutions to combat healthcare challenges in the country. The companies are offering a broad scope of solutions including automation of medical diagnosis, automated analysis of medical tests, detection and screening of diseases, wearable sensor based medical devices and monitoring equipment, patient management systems, predictive healthcare diagnosis and disease prevention. A challenge has been cited quite often while developing these solutions.


Are we ready for the AI driven precision healthcare revolution?

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Precision healthcare adds an efficiency and accuracy to healthcare treatments. With precision healthcare, Doctors can potentially develop targeted precise treatment and therapies for a population as well as an individual. This can improve patient treatment to large populations in countries like India. Current healthcare systems are primarily focussed on having treatments and solutions that can treat large population with similar symptoms. It's based on evidence and data which comes from a series of medical tests on a patient.


12 Artificial Intelligence Based Healthcare Startups in India

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Taking care of human health is a quite intricate job that requires broad and multiple aspects of the healthcare industry to work together. Healthcare industry is already overburdened with the exploding population and lack of trained doctors. The ratio of doctor to patients in India is 1:1700 which is far higher than the recommended ratio of 1 in every 1000 patients by WHO. The spontaneous increase in the count of efficient healthcare providers is not possible. But the access to intelligent and smart technologies can enhance the productivity and precision of existing ones in serving more patients in a specific time, with the ease to improve healthcare outcomes and in lowering the healthcare expense.