One of the hottest tech trends these days is artificial intelligence (AI), with researchers looking into the use of AI for applications ranging from autonomous vehicles to financial management, to healthcare. The healthcare industry is often at the forefront of innovation and technological advances due to the wealth of medical devices, equipment and processes that permeate the industry. But AI in particular seems poised to transform the way we collect, understand and use data on patient health, healthcare services and historical health data to revolutionize medical diagnostics, treatment and research. What makes AI so suitable for use in medical research and the healthcare industry? Largely, the appeal of AI is its ability to collect, analyze and make sense of vast amounts of unstructured and variable data--especially text, statistical numbers, and visual images--quickly and often more accurately than a human being.
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.
Artificial intelligence (AI) has demonstrated great progress in the detection, diagnosis, and treatment of diseases. Deep learning, a subset of machine learning based on artificial neural networks, has enabled applications with performance levels approaching those of trained professionals in tasks including the interpretation of medical images and discovery of drug compounds (1). Not surprisingly, most AI developments in health care cater to the needs of high-income countries (HICs), where the majority of research is conducted. Conversely, little is discussed about what AI can bring to medical practice in low- and middle-income countries (LMICs), where workforce shortages and limited resources constrain the access to and quality of care. AI could play an important role in addressing global health care inequities at the individual patient, health system, and population levels.
A new microscope will use image recognition software and machine learning technology to identify and count malaria parasites in a blood smear. The EasyScan GO, announced at MEDICA, the medical industry's leading trade fair, is the result of a partnership between the Global Good Fund, a Seattle-based group funded by philanthropist Bill Gates, and Motic, a China-based company that specializes in manufacturing microscopes. Field tests have demonstrated that the machine learning algorithm is as reliable as an expert microscopist in fighting the spread of drug resistant malaria.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The authors received no specific funding for this work. Competing interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: EV has received speaking fees from SwissRe, Novartis R&D Academy, and Google Netherlands. IGC served as a consultant for Otsuka Pharmaceuticals advising on the use of digital medicine for its Abilify MyCite product. IGC is supported by the Collaborative Research Program for Biomedical Innovation Law, which is a scientifically independent collaborative research program supported by Novo Nordisk Foundation.