VITech is pleased to reveal the results of the State of data science and ML in healthcare survey that we conducted on LinkedIn in 2019. The survey sought to look into the scope and patterns of data science and machine learning adoption in the healthcare industry. Over 50 qualified respondents represented a variety of company sizes, from startups to corporations with more than 10,000 employees, in the pharma, care sector, biotechnology, and medical device development. Among them: are C-level execs, directors, and VPs (50% of the pool), as well as ML engineers, data scientists, and software developers. The surveyed have a firm grasp of the industry’s challenges and objectives, enabling us to analyze and assess the trends and the actual state of tech in healthcare.
Around 50% of respondents surveyed in State of data science and ML in healthcare report they have already adopted AI. However, data remains the key concern. Finding reliable and relevant data, data evaluation, data extraction, data processing and cleansing, and data transfer are significant challenges for AI initiatives. Others wrestle with visualizing large datasets to enable more efficient analytics and decision-making.
At the same time, although AI adoption is overgrowing among the healthcare industry leaders, half of the respondents note their companies do not use any of the data science and machine learning methods to drive either research or business outcomes. However, part of them indicates that they are planning to or are already exploring solutions relevant to their needs. Current requests are trend identification, diagnostic imaging results, prognostics, and predictive analytics.
Machine learning enables healthcare companies to optimize a wide range of business processes, but analytics and image analysis are reported as the most common tasks by 45% of the surveyed.
The respondents report progressively migrating enterprise workloads to the cloud to take advantage of its network of data storage, data processing, and machine learning services. Amazon web services and Microsoft Azure remain the dominant cloud providers in healthcare, with 40% and 30% of the market. They are also the platforms of choice for data science and machine learning tasks, with Python and R as the top open-source programming languages for data analysis.
Among the most repeatedly featured ML platforms and libraries are Keras, PyTorch, Tensorflow, and OpenCV. Django, Docker, SQL Server, and Apache Kafka have been picked as data analysis and data streaming tools of preference, too. Security-wise, the leaders are DarkTrace and Sophos.
REAL Space Navigator, Pharmapendium, Reaxys, and Scifinder are reported for research among healthcare-specific tools. At the same time, Qlik Sense and Spotfire are primarily used for decision-making and data visualization, and Teradata and Databricks — are for data analytics.
For sure, organizations in healthcare continue to rely on a wide range of statistical tools, including the ones developed in-house and Excel sheets.
Around 50% of those organizations and teams who have not applied any data science and machine learning techniques in 2019 plan to kick off their AI adoption journey next year. Primarily, they plan to cover a variety of prediction tasks, from volume/demand predictions and prognostics to predictive toxicology and predictive risk alerts.
Other notable tasks are CAD and image analysis, NGS for pharmacovigilance, staff performance analysis to cut man-hours (i.e. better case processing), clinical trial analysis and optimization, drug design, analysis of sales team data, and PLC communication.
Note: 57% of the surveyed report that their organizations do not struggle with IT any infrastructure challenges; 30% report otherwise.
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