The Role of Radiologists and the Future of Artificial Intelligence
The dataset consists of 29,000 documents related to the new virus and the broader family of coronaviruses, 13,000 of which have been processed so that computers could read basic data, information about the authors, and their affiliations.
Challenges
Just like any emerging trend, data science is facing certain challenges. In healthcare, the ethical aspect comes to the fore. Philips’ Future Health Index 2019 study found data privacy is a significant barrier to digital health adoption. People want to know how secure is the information they upload to a computer for analysis or send to their doctor.
It will be easier for people to embrace change in healthcare when everyone understands that innovation is not there to replace medical staff. Digital technology only helps professionals make the most accurate and informed decisions. The neural network can identify the illness based on its symptoms and suggest prescription options, but patients can rest assured that the doctor still has the last word — only healthcare professionals are authorized to make the final diagnosis and determine the necessary treatment.
Healthcare data science struggles with not just ethical but also technical concerns. Too often, there is a lack of complete, consistent, representative, pre-labeled data that could be used to train a machine to analyze and classify materials and make predictions. Health information is still collected and processed manually. It is a laborious, monotonous, and time-consuming process that often lacks resources.
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Even if there is enough data, problems can come up at the stage of implementing the ready-to-use algorithms. Many illnesses evolve over time, and common disorders might display a whole variety of signs. It is impossible to predict how the system is going to behave if it confronts an unusual situation. Most algorithms can only pass a final verdict — yes or no, norm or pathology. Not a single algorithm can yet report: “I have never seen this, and I do not know what it is.” So, computers should be taught not only to give an answer but also to assess how reliable the results are.
What is the future of health data?
The deployment of new technologies can be a lengthy process complicated by ethical, legal, and financial issues. However, the very fact that data science is in such high demand in healthcare proves that it does help us deal with problems more efficiently. Government agencies have started to embrace this, digitizing healthcare in public-funded programs, while big companies keep hiring data science specialists.
According to CB Insights, every month, investors are finding more and more companies and startups working on AI-based healthcare solutions. In the first quarter of 2020, the amount of venture capital investments in healthcare AI startups globally exceeded $980 million. Researchers expect that shortly innovations will become part of doctors’ daily routine and help improve the quality of life around the world.
Aggregation of research works
Data extraction is a crucial task of natural language processing (NLP) to discover and extract important knowledge hidden in the unstructured clinical data. Every single day, thousands of new medical articles are published on the Internet, describing the nature of illnesses and methods of their treatment. Each scientific work certainly makes a huge contribution to healthcare evolution; each new discovery brings humanity closer to overcoming another disease.
However, there are two sides to the same coin. The main obstacle to the effective use of scientific articles is that there are too many of them, and one keyword search is not enough. As a result, researchers need costly and time-consuming text review. In 2020, Google teamed up with Microsoft, the National Library of Medicine, and the Allen Institute for AI to release the Covid-19 Open Research Dataset (CORD-19). It will enable the global AI community to use text and data mining approaches, as well as NLP techniques to find solutions in response to the pandemic.