High-quality medical systematic reviews require comprehensive literature searches to ensure the recommendations and outcomes are sufficiently reliable. Indeed, searching for relevant medical literature is a key phase in constructing systematic reviews and often involves domain (medical researchers) and search (information specialists) experts in developing the search queries. Queries in this context are highly complex, based on Boolean logic, include free-text terms and index terms from standardised terminologies (e.g., the Medical Subject Headings (MeSH) thesaurus), and are difficult and time-consuming to build. The use of MeSH terms, in particular, has been shown to improve the quality of the search results. However, identifying the correct MeSH terms to include in a query is difficult: information experts are often unfamiliar with the MeSH database and unsure about the appropriateness of MeSH terms for a query. Naturally, the full value of the MeSH terminology is often not fully exploited. This article investigates methods to suggest MeSH terms based on an initial Boolean query that includes only free-text terms. In this context, we devise lexical and pre-trained language models based methods. These methods promise to automatically identify highly effective MeSH terms for inclusion in a systematic review query. Our study contributes an empirical evaluation of several MeSH term suggestion methods. We further contribute an extensive analysis of MeSH term suggestions for each method and how these suggestions impact the effectiveness of Boolean queries.

The artificial intelligence and automation fields are growing at remarkable rates. According to a report from Grand View Research, the global AI industry is currently valued at almost $25 billion, and stands to grow by 46% between 2019 and 2025. Grand View Research also predicts that industrial automation will grow by 8.6% during the same period. As these fields expand, job opportunities are becoming more and more abundant, offering plenty of entry points for those who are not only technically inclined but also inventive and creative. Indeed, experts predict that AI and automation will impact nearly every major industry, which means career prospects are innumerable.

On 11 October, 2019 at KIN Center for Digital Innovation, we talked about the impact of artificial intelligence (AI) on work, delving into the case of the Dutch police and predictive policing. Professor Marleen Huysman opened the floor by comparing different perspectives on the study of AI. She discussed the labor perspective which focuses on macro-level changes on the whole labor market; the critical perspective which discusses ethical issues around AI in a theoretical/conceptual manner; the business perspective which talks about the potential of AI in tackling various challenges around organisation and innovation. Then Marleen introduced the practice perspective – a stance taken by the researchers at KIN – which looks into the actual practices around the design, control, and use of AI with the aim of going beyond the hype and working together with practitioners. After the brief yet comprehensive overview of different vantage points to research on AI and work, the floor was given to Dick Willems, a data scientist at the Dutch police and creator of the Dutch predictive policing algorithm: 'Crime Anticipation System (CAS)'.

Artificial intelligence--the mimicking of human cognition by computers--was once a fable in science fiction but is becoming reality in medicine. The combination of big data and artificial intelligence, referred to by some as the fourth industrial revolution,1 will change radiology and pathology along with other medical specialties. Although reports of radiologists and pathologists being replaced by computers seem exaggerated,2 these specialties must plan strategically for a future in which artificial intelligence is part of the health care workforce.

Artificial intelligence--the mimicking of human cognition by computers--was once a fable in science fiction but is becoming reality in medicine. The combination of big data and artificial intelligence, referred to by some as the fourth industrial revolution,1 will change radiology and pathology along with other medical specialties. Although reports of radiologists and pathologists being replaced by computers seem exaggerated,2 these specialties must plan strategically for a future in which artificial intelligence is part of the health care workforce. Radiologists have always revered machines and technology. In 1960, Lusted predicted "an electronic scanner-computer to examine chest photofluorograms, to separate the clearly normal chest films from the abnormal chest films."3