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©2023 American Medical Writers Association. All rights reserved.  
ISSN 2163-5315

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ABSTRACT 
Medical writing is a process that generates a variety of docu-

ments in the biomedical domain, including but not limited 

to clinical reports, regulatory reports, protocol documents, 

patient narratives, plain language summaries, and so on.1 

Medical writing is complex and time-consuming because 

a writer must refer to multiple sources, sift through a large 

volume of documents, maintain data integrity, perform 

review of literature, do interpretation of results, summarize, 

and so on.

 These challenges can be addressed and minimized sub-

stantially by adopting artificial intelligence, specifically cog-

nitive search, natural language processing (NLP), and natural 

language generation (NLG) models and other techniques. 

Given the recent advances in language models for NLG, the 

time is ripe for a product in the medical writing domain that 

integrates and automates search capabilities, provides cogni-

tive processing, and generates content using NLG.

 This white paper takes scientific manuscript writing as an 

example to provide insights into the way NLP and NLG can 

augment, automate, and expedite the process of writing a 

wide variety of biomedical documents. It looks at the current 

limitations of technology and ways to address those. Finally, it 

provides recommendations on how these technologies can be 

used to create a single system or product. Such an approach 

has the potential to expand into multiple areas in the biomed-

ical domain, with medical writing as the first challenge.

INTRODUCTION
CURRENT MEDICAL WRITING MARKET
According to Grand View Research, the global medical  

writing market size that was valued at US $3.4 billion in 2019 

is expected to expand to US $7.77 billion by 2027 at a com-

pound annual growth rate of 10.9%.2 The cost spent on  

content generation continues to rise.

EXISTING MEDICAL WRITING PROCESS
Medical writing is a complex and manually intensive process. 

The process of medical writing involves the following steps

a) Understanding the content brief

b) Review of literature

c) Collation of the results, methods, and discussion  

sections

Deepak Palasamudram1; Karun S. Karunakaran2; Prakhar Gaur3; Akshatha Miyal Kamath2; Pramit Saha4; Tina Purushotam5/ 
1Associate Vice President, Architecture and Design Group, Healthcare, Insurance and Life Sciences, Infosys Limited, India; 
2Architect, Architecture and Design Group, Healthcare, Insurance and Life Sciences, Infosys Limited, India; 3Consultant, Life 
Sciences Domain Consulting Group, Infosys Limited, India; 4Project Manager, Architecture and Design Group, Healthcare,  
Insurance and Life Sciences, Infosys Limited, India; 5Digital Specialist Engineer, Architecture and Design Group, Healthcare, 
Insurance and Life Sciences, Infosys Limited, India

Leveraging Artificial Intelligence, Natural Language Processing,  
and Natural Language Generation in Medical Writing

ARTICLE

GLOSSARY
Natural Language Processing (NLP): The branch of 
artificial intelligence (AI) that enables computers to process 
human language and understand the meaning, intent, and 
sentiment of the text, much like a human being can.

Natural Language Generation (NLG): The branch of AI 
that enables computers to produce human language that 
approximates content generated by a human being.

Recommendation Model: A system that uses machine 
learning to predict content that is relevant for a user in a 
given context. The predictions are often combined with a 
ranking system that enables users to see the most relevant 
recommendations first.

Named Entity Recognition (NER): A process by which 
text is classified into predefined categories like drug name, 
disease name, location. Also, depending on the context, 
it can differentiate between “apple” (fruit) and “Apple” 
(corporation).

Large Language Model (LLM): Large Language Models 
(LLMs) are artificial intelligence tools that can read, 
summarize and translate texts and predict future words in 
a sentence letting them generate sentences similar to how 
humans talk and write.11  Eg., GPT-3, GPT-J, BART, BERT, t5.

Natural Language Query Understanding (NLQU): This 
is a capability of the search system to understand a search 
query written in natural language. This is achieved by LLM 
based search systems. Eg. “What is the second largest land 
animal in the world?”

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AMWAJournal.org     46Leveraging AI, NLP, and NLG in Medical Writing

d) Authoring the manuscript and maintenance of data 

integrity in the process

e) Reviewing the authored content

f) Copy editing

g) Approval and sign off

h) Electronic publishing

 Medical writers spend 2 to 3 weeks researching across 

multiple data sources and a large corpus of documents 

(nearly 1 million new articles are added yearly to just 

PubMed).3

 The review of literature is the most time-consuming 

step in the medical writing process. This requires a domain 

expert to first search for and then read through the text of 

the articles on a particular subject area. The goal of this step 

is to synthesize the existing knowledge in a particular sub-

ject area. In the case of writing a scientific manuscript for 

a clinical trial, the review of literature must cover several 

subtopics in the therapy area of concern. All the subtop-

ics require individualized search strategies irrespective of 

the therapeutic area. Medical writers use several literature 

databases like PubMed, Scopus, Ovid, and Cochrane. This 

also introduces the risk of missing out on relevant literature, 

making this task not only time-consuming, but also error-

prone. The aforementioned tasks require multiple  

individuals to complete it in a reasonable amount of time, 

each one concentrating on a particular subset of the  

overall document.

 The final challenge lies with the summarization step, 

when information gleaned from several published articles is 

summarized. The risks here are missing the important points 

as well as accidentally not including a relevant reference.

 In writing the results section of a manuscript, data may 

need to be collated from a source document like a clini-

cal study report (CSR) and adding it to the manuscript in 

a particular format. This can involve aggregating and sum-

marizing the data, creating plots, or writing a narrative for 

a particular set of data. A great example are the tables for 

adverse events. This step may introduce quality issues if not 

done carefully. Obviously, the power of using a computer 

to automate data analytics is well known, and natural lan-

guage generation (NLG) provides tools to create narratives 

summarizing tabular data accurately.

 Products with authoring workflows that allow collabora-

tion on a single document by multiple people have been in 

use for more than a decade.4 In addition to these, functional-

ities like referencing and text formatting according to  

journal requirements have also been in use. These capabil-

ities can come from various tools and techniques, which 

would require integration of many products or tools into a 

single system.

ROLE OF ARTIFICIAL INTELLIGENCE IN  
MEDICAL WRITING
A system that can automate and assist with these tasks 

would help mitigate many of the challenges and risks 

described before. Artificial intelligence (AI) has several inter-

esting possibilities for transforming any industry. Nowhere 

are its applications more relevant than for life sciences and 

pharmaceutical, regulatory, and medical writing for creating 

documents such as scientific manuscripts, fact sheets, litera-

ture reviews, disease awareness, and oral posters.

 A canonical use case for the application of AI is the 

process of the review of literature that is done as part of 

research work. In the review of literature process, the 

researcher is required to use their language and domain 

knowledge to summarize the various published articles on 

a topic. This is done to summarize the state of the art in the 

field. NLG models are being used to automate this step; at 

the same time, manual intervention is required before the 

machine-generated text can be submitted for publication.

 Many functionalities are required to automate the  

medical writing process that are elaborated on in the  

following sections.

Cognitive Search for Literature Survey and 
Recommendation
The review of literature requires multiple subtopics in a dis-

ease/therapeutic area to be comprehensively covered. This 

requires individualized search strategies for each subtopic, 

with the search itself carried out across multiple databases. 

The next task is to read through the top hits for each subtopic 

to identify the relevant content in that published article.

 AI or machine learning (ML) can help in automating 

the literature search, content extraction, content enrich-

ment (which includes named entity recognition [NER]), and 

intent detection in the context of life sciences and pharma-

ceuticals. This enables advanced unified searching across 

multiple data sources and databases.

 Secondly, once trained, the recommendation models 

are used to identify the relevant sentences or paragraphs in 

the articles. Automation of this step can enhance efficiency 

of the most time-consuming component in the literature 

review process. In addition to automation, the system can 

perform citation management, an essential part of any med-

ical or technical document.

Leveraging AI, NLP, and NLG in Medical Writing

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AMWAJournal.org     47Leveraging AI, NLP, and NLG in Medical Writing

Narrative Generation
The generation of narratives from structured data by apply-

ing NLG has been in the life sciences domain for more than 

a decade. Previously, it was done using hardcoded text as 

part of code, and now, NLG models can generate text up 

front for the structured data that is being processed. The text 

or narrative generated by the NLG model can, depending on 

the models used, involve ML algorithms (deep learning) or 

preset parameters. The output from either of these or a com-

bination of both is a narrative as would have been written by 

a medical writer.5

Summarization
The most recent developments in the NLG space have 

enabled models to summarize large texts. The initial models 

were trained and built using news articles because they pro-

vide human-generated text and summaries. Now, models 

are being trained on the published medical corpus avail-

able as peer-reviewed scientific articles. Further refinement 

of the models specific to diseases and drugs are needed in 

automating medical writing.

 There are 2 types of summarization techniques possible 

using NLG models: extractive and abstractive.

 Extractive summarization involves identifying import-

ant subsets of sentences from the original text in toto and 

forming a summary comprising such sentences. This type of 

summarization is useful if the author decides to select mul-

tiple sources from the recommendations and to rewrite the 

text on their own after the summary gets generated.

 Abstractive summarization reproduces important mate-

rial in a new way after examination and interprets the text 

using NLG capabilities, simulating how humans do a review 

of literature.6,7

 Abstractive summarization mimics how an author 

would write a synthesis of existing literature in their own 

words along with the references used. Examples of such 

models include GPT-3, t5, BERTs, and BARTs.8 Abstractive 

summary is useful when the authors want an abstract of the 

selected recommendations. This kind of summary, along 

with reference metadata, addresses issues related to plagia-

rism because this is not an exact reproduction of text from 

the sources but a generation of original text.

 Figure 1 depicts one such example of extractive and 

abstractive summarizations.

KEY CHALLENGES IN APPLYING AI TO  
MEDICAL WRITING
The key challenges to applying AI in medical writing include

• Ingesting documents from diverse sources and variety of 

formats. Apart from a pharmaceutical company’s inter-

nal data sources, there are multiple external sources like 

PubMed articles, regulatory documents, clinical trial 

documents, protocols, clinical study reports, and press 

releases. This necessitates dealing with different doc-

ument formats and structures like native PDFs, Docx, 

XML, HTML, and scanned documents.

• Understanding document semantics and content, 

extracting key entities unambiguously, capturing syn-

onyms based on scientific ontologies, identifying con-

texts and intents from medical content in the context 

of life sciences and pharmaceuticals. This requires 

compositional semantic analysis that includes word 

sense disambiguation and relationship extraction that 

is relevant in biomedical literature.

• Reranking cognitive search results for better search 

relevance. This requires adoption of learning to rank 

Figure 1. Extractive and abstractive summaries from given input text.

'\ 
Extractive Summarization: 

Psoriasis vulgaris is a chronic inflammatory condition 
associated with significant morbidity and mortality. 
Plaque psoriasis is the most common form of psoriasis 
vulgaris and classically presents as discrete, 
erythematous plaques with an overlying silvery scale on 
extensor surfaces. 

./ 

" 

Input: 
Psoriasis vulgaris is a chronic inflammatory condition 
associated with significant morbidity and mortality. 
Plaque psoriasis is the most common form of psoriasis 
vulgaris and classically presents as discrete, 
erythematous plaques with an overlying silvery scale 
on extensor surfaces. 
The most common form of psoriasis is chronic plaque 
psoriasis, which is characterized by stable and 
localized erythematous scaly plaques that are well 
demarcated from normal skin. Psoriasis is a common 
inflammatory skin disease that causes significant 
stress and morbidity.

Abstractive Summarization: 

Psoriasis vulgaris (PsV) is a chronic inflammatory skin 
disease characterized by erythematous scaly plaques 
with an overlying silvery scale on extensor surfaces. 

/ 

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AMWAJournal.org     48Leveraging AI, NLP, and NLG in Medical Writing

also know as machine-learned ranking. This process 

re-ranks results from search engines in the medical 

writing context for content such as the mechanism of 

action for drugs or disease epidemiology, etc.9

• Combining multiple modes of intelligence such as 

natural language processing (NLP), NLG, deep learn-

ing, language models, lexicons, and ontologies into a 

state-of-the-art AI-based platform for medical writing.

• Removing biases from algorithms. Potential biases can 

creep in at various steps, from the curation of training 

datasets, to feature engineering, model choice, and 

implementation. Detecting and removing algorithmic 

biases will entail evaluating it via a thorough under-

standing of the algorithm’s role and the context in 

which it is deployed.

BRINGING IT ALL TOGETHER
As discussed earlier, one of the objectives of this article is 

to define the architecture and components of a system or 

product that will automate the process of medical writing 

significantly. Such a system should have the end-to-end 

ability to ingest documents, identify the entities in those 

documents, provide them as search results based on user 

queries, and generate summaries based on user-selected 

documents. These features require adoption of the various 

AI techniques discussed previously.

 AI horizons have seen a strategic shift from conventional 

ML (with a focus on augmenting intelligence) to deep learn-

ing (enabling higher accuracy and predictability), and now 

to the responsible, transparent generative AI.

 The key emerging trends for language processing and 

generation include

• Adoption of deep learning and transfer learning archi-

tectures driving accuracy, performance, and speed.

• The NLP shifts from extraction of isolated entities to 

abstractive reasoning and language models.

• Using models for text critiquing, information retrieval, 

  question answering, summarization, gaming, text  

generation, and translation.

 With state-of-the-art pretrained language models (eg, 

GPT-3, GPT-J, BART, BERT) that can be fine-tuned for the 

biomedical domain, the system can generate human-like 

summarizations and narratives.10 Consequently, the text 

summarization exercise and the final document generation 

can be reduced to a few days rather than a few weeks, even 

after accounting for the final manual review and approval 

processes.

 Moreover, with all workflows automated, the scope of 

error is minimized, contrasted with the current manual  

process (Figure 2).

 To bring about these efficiencies, the AI-led platform for 

life sciences is envisaged to encompass the following key 

features:
• Unified search across multiple internal and external 

databases
• Built in deep learning models for article recommen-

dation in the context of pharmaceutical clinical trials, 
regulatory intelligence, and medical research

• State-of-the-art language models fine-tuned for life 
sciences for text summarization and NLG tasks

• Real-time data ingestion of structured or unstructured 
documents from varied data sources (scientific articles  
from PubMed, regulatory sources like the US Food 
and Drug Administration (FDA), European Medicines 
Agency, CSRs, and protocol documents, etc)

• NLP-based automatic document structure extraction, 
content enrichment, and sentiment analysis

• Dynamic document editing features leveraging  
scientific lexicons and ontologies

• Workflows for collaborative medical authoring
• Content citations (ability to refer to original sources 

from a machine-generated summary)
• Templatization of the final document based on  

the need 

Figure 2. Automated process of medical writing. AI, artificial intelligence; CSR, clinical study report.

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AMWAJournal.org     49Leveraging AI, NLP, and NLG in Medical Writing

 Medical writing typically involves authoring contents 

like medical manuscripts, posters, or clinical study reports 

with predefined templates for each content type. An author-

ing template is not just a bare-bone skeleton for con-

tent authoring but a composite of individual sections, the 

onboarding of which entails data ingestion, article recom-

mendation, and content summarization steps (Figure 3).

 Let us illustrate this through an example of the introduc-

tion section of a typical manuscript. This section includes 

content primarily from PubMed articles contextualized for 

disease description, epidemiology, burden of disease, and a 

drug mechanism of action.

 The following activities are required for the generation of 

an introduction section of the manuscript.

• For data ingestion, PubMed articles are considered, 

and indexing is configured for relevant article sections 

like “Abstract” and “Introduction.” NLP pipelines are 

used for the classification of sentences as belonging 

to categories like “description of disease,” “burden of 

disease,” “disease epidemiology,” and “mechanism of 

action.” These create labels and do NER for diseases, 

drugs, molecules, and so on.

• For article recommendation, natural language query 

understanding pipelines for intents like classification 

contexts are defined. Search ranking rules and boost-

ing criteria are refined as required.

• For content summarization, based on the section spe-

cific summarization or narrative needs, the platform 

evaluates the available language models. For config-

uration initial training, samples are curated for plat-

form-suggested language model fine-tuning, and 

pipelines are defined for subsequent active learning.

 Figure 4 provides a schematic view of the platform archi-

tecture. The document sources will not only be external in 

nature like PubMed, Ovid, and ClinicalTrials.gov, but also 

Figure 3. Section onboarding in 
medical writing platform. NLP, 
natural language processing.

Figure 4. A schematic view 
of the platform architecture. 
API, application programming 
interface; EMA, European 
Medicines Agency; FDA, 
United States Food and Drug 
Administration; ML, machine 
learning; NLP, natural 
language processing.

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AMWAJournal.org     50Leveraging AI, NLP, and NLG in Medical Writing

regulatory documents like those published by the FDA and 

document sources that are internal to any organization 

deploying the platform.

 During the ingestion of the documents, document  

structure is extracted and tagged with metadata that helps 

in indexing and classifying the content for future use. Such 

extraction includes classification of sentences and para-

graphs and sections as dealing with different drugs,  

diseases, and other biomedical terms.

 When a user searches for documents, the questions 

posed by the user in plain English are translated into 

machine-readable queries that are then searched against 

the indexed documents. The results are then ranked accord-

ing to the rules and boosting criteria used and returned to 

the user as recommendations.

 Once the user selects the documents identified for  

summarization, NLG is used to generate extractive or 

abstractive summaries of the selection.

CONCLUSION
AI and ML, combined with NLP and NLG, promises to ben-

efit the medical writing process by reducing the manual 

aspects of the work by automating many steps, in addition 

to improving quality and reliability. The time and effort thus 

saved can be substantial to large organizations that often 

spend a considerable amount of both during the lifecycle  

of a drug.

Author declaration and disclosures: The authors acknowl-
edge the team’s learning from building the Cognitive Search and 
Medical Writing Platform at Infosys.

Author contact: DEEPAKPN@infosys.com

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