Medical Writing Artificial intelligence and Digital Health Medical writing in the era of artificial intelligence

Volume 28, Issue 4 - Artificial intelligence and Digital Health

Medical writing in the era of artificial intelligence


The increasing amount of data available together with advances in computer science are converting computers from simple tools that execute commands into self-taught, self correcting machines that make decisions. This is the beginning of the era of artificial intelligence (AI) that promises a revolution in the way we live and work. AI has entered apace the fields of healthcare and medicine and has started to affect the work of medical
writers. A recent survey has revealed that 40% of scientists are still unfamiliar with the use of AI in healthcare with opinions ranging from panic to over-optimism. These are big challenges for all medical writers (MWs). This article discusses the critical role MWs play in an AI-driven healthcare, describes how AI can empower medical writers in various domains (regulatory, medical affairs, redaction, and publishing), and highlights the importance of staying up-to-date with the AI world.

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  1. Hwang T. Computational power and the social impact of artificial intelligence. 2018 Mar 23 [cited 2019 Aug 12]; Available from: Available from: 08971v1
  2. Deep Blue (chess computer). In: Wikipedia [Internet]. 2019 [cited 2019 Aug 12]. Available from: https: Available from: title=Deep_Blue_(chess_computer) &oldid=909996580
  3. Silver D, Schrittwieser J, Simonyan K, et al. Mastering the game of Go without human knowledge. Nature. 2017 18;550(7676):354–9.
  4. Brown N, Sandholm T. Superhuman AI for multiplayer poker. Science. 2019 Aug;365(6456):885–90.
  5. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017 Apr;69S:S36–40.
  6. Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp. 2018 Oct 24;2(1):35.
  7. Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019;394(10201) 861–7.
  8. Brinker TJ, Hekler A, Enk AH, Berking C, Haferkamp S, Hauschild A, et al. Deep neural networks are superior to dermatologists in melanoma image classification. Eur J Cancer Oxf Engl. 1990. 2019 Aug 8;119:11–7.
  9. Qureshi F, Krishnan S. Wearable hardware design for the internet of medical things (IoMT). Sensors. 2018 Nov 7;18(11).
  10. Williams Brian. Enabling better healthcare with artificial intelligence [Internet]. Next In Tech. 2017 [cited 2019 Aug 14]. Available from: Available from: emerging-technology/ai-in-healthcare/.
  11. AI use in healthcare increasing slowly worldwide [Internet]. Medscape. [cited 2019 Aug 14]. Available from: Available from: 912629.
  12. de la Iglesia D, García-Remesal M, Anguita A, Muñoz-Mármol M, Kulikowski C, Maojo V. A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from PloS One. 2014;9(10):e110331.
  13. Harrer S, Shah P, Antony B, Hu J. Artificial intelligence for clinical trial design. Trends Pharmacol Sci. 2019 Aug;40(8):577–91.
  14. Zhavoronkov Α, Ivanenkov YA, Aliper A, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol. 2019;37(9):1038–40.
  15. Martínez MJ, Razuc M, Ponzoni I. MoDeSuS: A machine learning tool for selection of molecular descriptors in QSAR studies applied to molecular informatics. BioMed Res Int. 2019;2019:2905203.
  16. Zhang H, Mao J, Qi H-Z, Ding L. In silico prediction of drug-induced developmental toxicity by using machine learning approaches. Mol Divers. 2019 Sep 5; Available from: 09991-y
  17. Lavecchia A. Deep learning in drug discovery: Opportunities, challenges and future prospects. Drug Discov Today. 2019 Aug;S1359-6446(19)30282-X.
  18. Fisher D, Parisis N. Social influence and peer review: Why traditional peer review is no longer adapted, and how it should evolve. EMBO Rep. 2015 Dec;16(12):1588–91.
  19. Nguyen VM, Haddaway NR, Gutowsky LFG, et al. How long is too long in contemporary peer review? Perspectives from authors publishing in conservation biology journals. PloS One. 2015;10(8):e0132557.
  20. Wicherts JM. Peer Review Quality and Transparency of the Peer-Review Process in Open Access and Subscription Journals. PloS One. 2016;11(1):e0147913.
  21. Mrowinski MJ, Fronczak P, Fronczak A, Ausloos M, Nedic O. Artificial intelligence in peer review: How can evolutionary computation support journal editors? PloS One. 2017;12(9):e0184711.
  22. SpotOn 2016 Report [Internet]. BMC Events. [cited 2019 Aug 13]. Available from: Available from: http://events.biomedcentral. com/spoton-2016-report/
  23. Yahoo! Sports uses NLG to produce draft over 70 million reports and match recaps [Internet]. Automated Insights. [cited 2019 Aug 7]. Available from: Available from: customer-stories/yahoo/
  24. Samuel S. How I’m using AI to write my next novel [Internet]. Vox. 2019 [cited 2019 Sep 6]. Available from: Available from: 2019/8/30/20840194/ai-artfiction- writing-language-gpt-2
  25. DuBay WH. The Principles of Readability [Online]. [Internet]. 2004 [cited 2019 Aug 13]. Available from: Available from: ED490073.pdf
  26. Dixon J. Readability tools: are they useful for medical writers? [video]. 2017 [cited 2019 Aug 13]. Available from: Available from: 10/17/video-readability-tools-are-theyuseful- for-medical-writers/
  27. Min YH, Park H-A, Lee JY, et al. Automatic generation of nursing narratives from entity-attribute-value triplet for electronic nursing records system. Stud Health Technol Inform. 2014;201:452–60.
  28. Polepalli Ramesh B, Houston T, Brandt C, Fang H, Yu H. Improving patients’ electronic health record comprehension with NoteAid. Stud Health Technol Inform. 2013;192:714–8.
  29. Yim W-W, Yetisgen M, Harris WP, Kwan SW. Natural language processing in oncology: A review. JAMA Oncol. 2016 Jun 1;2(6):797–804.
  30. Savova GK, Danciu I, Alamudun F, et al. Use of natural language processing to extract clinical cancer phenotypes from electronic medical records. Cancer Res. 2019 Aug;canres.0579.2019.
  31. Pons E, Braun LMM, Hunink MGM, Kors JA. Natural language processing in radiology: A systematic review. Radiology. 2016 May;279(2):329–43.
  32. Kreimeyer K, Foster M, Pandey A, et al. Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review. J Biomed Inform. 2017;73:14–29.
  33. Virdee N. How AI tech is changing regulatory writing [Internet]. Certara. 2017 [cited 2019 Aug 5]. Available from: Available from: how-ai-tech-is-changing-regulatorywriting/
  34. Pereda M, Estrada E. Machine learning analysis of complex networks in hyperspherical space. ArXiv180405960 Phys [Internet]. 2018 Apr 16 [cited 2019 Aug 13]; Available from: Available from:
  35. Smith S. AI for Medical Affairs White Paper [Internet]. [cited 2019 Aug 8]. Available from: Available from: affairs-white-paper
  36. Ganti L. How can AI technologies help streamline medical affairs processes? [Internet]. Innoplexus. 2019 [cited 2019 Aug 8]. Available from: Available from: ai-technologies-help-streamline-thedecision- making-process-for-medicalaffairs/
  37. Haycock N, Dawes K. Collecting metrics in medical writing – the benefits to you and your business. Med Writ. 2019 Jun 1;28:85–7.
  38. England JR, Cheng PM. Artificial Intelligence for Medical Image Analysis: A Guide for Authors and Reviewers. AJR Am J Roentgenol. 2019 Mar;212(3): 513–9.



Table of Contents
Artificial intelligence and digital health
President's Message
Medical writing in the era of artificial intelligence
Blockchain in healthcare, research, and scientific publishing
Embracing a new friendship: Artificial intelligence and medical writers
Drug development and medical writing in the digital world
Intelligent use of artificial intelligence for systematic reviews of medical devices
What medical writers need to know about regulatory approval of mobile health and digital healthcare devices
Digitalisation in long-term care: An issue for medical writers?
An introduction to medical affairs for medical writers
A primer on anonymisation
Sound, microphone, action: Podcasts for medical writers
Regulatory Matters
News from the EMA
Lingua Franca and Beyond
In the Bookstores
Getting Your Foot in the Door
Medical Devices
Veterinary Medical Writing
Teaching Medical Writing
Journal Watch
Medical Communications and Writing for Patients
Good Writing Practice
Out on Our Own
Upcoming issues of Medical Writing


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Editoral Board


Raquel Billiones


Evguenia Alechine

Jonathan Pitt

Managing Editor

Victoria White

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Alicia Brooks Waltman

Associate Editors

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Claire Chang

Barbara Grossman

Sarah Milner

John Plant

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Section Editors

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Jennifer Bell


Nicole Bezuidenhout 

Digital Communication

Somsuvro Basu

EMWA News 

Ana Sofia Correia 

Gained in Translation

Ivana Turek

Getting Your Foot in the Door

Wendy Kingdom / Amy Whereat

Good Writing Practice

Alison McIntosh 

In the Bookstores

Maria Kołtowska-Häggström

Lingua Franca and Beyond

Maddy Dyer


Lisa Chamberlain-James

Medical Communications/Writing for Patients

Payal Bhatia

Medical Devices

Evguenia Alechine

My First Medical Writing

Anuradha Alahari

News from the EMA

Adriana Rocha


Tiziana von Bruchhausen


Clare ChangZuo Yen Lee 

Regulatory Matters

Sam Hamilton

Regulatory Public Disclosure

Claire Gudex

Teaching Medical Writing

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The Crofter: Sustainable Communications

Louisa Marcombes

Veterinary Writing

Editors Emeritus

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