How can you use text generation to improve healthcare?

Text generation is the process of creating natural language texts from data, such as keywords, summaries, or images. It is one of the most exciting and challenging applications of artificial intelligence (AI), especially with the recent advances in deep learning and transformers. Text generation can have a huge impact on healthcare, as it can help with tasks such as diagnosis, treatment, education, and research. In this article, you will learn how you can use text generation to improve healthcare in four ways.

1 Generate medical reports

One of the most common and useful applications of text generation in healthcare is to generate medical reports from images, such as X-rays, CT scans, or MRI scans. This can save time and resources for radiologists, who have to write hundreds of reports every day, and improve the accuracy and consistency of the reports. Text generation models can be trained on large datasets of images and their corresponding reports, and use transformers to capture the complex relationships between the visual and textual information. For example, a text generation model can take an X-ray image of a chest as input, and produce a report that describes the findings, such as the presence of pneumonia, pleural effusion, or fractures.

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Arman Assadi

Co-Founder & CEO of Steno.ai (Techstars '23) | Co-host of the Alfalfa podcast | Thinker, Builder, and Creator
Text generation can transform healthcare by streamlining medical report writing: 1. Efficiency: AI quickly drafts reports, allowing doctors more time for patient care. 2. Accuracy: AI ensures consistency and minimizes errors by using patient data. 3. Customization: Adapts to different medical fields’ reporting needs. 4. Up-to-Date Information: Incorporates the latest medical research and guidelines. 5. Language Translation: Makes reports accessible in multiple languages. 6. Predictive Analysis: Identifies health risks through data pattern analysis. This automation not only boosts efficiency but also enhances patient care quality.
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2 Generate clinical notes

Another application of text generation in healthcare is to generate clinical notes from speech or text inputs, such as patient interviews, symptoms, or lab results. This can help doctors and nurses to document their interactions with patients more efficiently and effectively, and reduce the burden of manual data entry. Text generation models can be trained on large datasets of clinical notes and their corresponding inputs, and use transformers to generate coherent and relevant texts that follow the medical standards and terminologies. For example, a text generation model can take a speech input of a patient describing their headache, fever, and cough, and produce a clinical note that summarizes the patient's history, diagnosis, and treatment plan.

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Dr. John Martin

Academician & Education Leader | Computer Science | Head of Curriculum | Jazan University | Pioneering Healthcare AI Innovation
AI-generated text can aid healthcare professionals in creating detailed and accurate clinical documentation. This includes patient histories, progress notes, discharge summaries, and other medical reports. These tools can help in saving time, reducing administrative burdens, and ensuring standardized, organized, and comprehensive records. However, it's crucial to note that while AI has tremendous potential in healthcare, it should complement and assist healthcare professionals rather than replace their expertise. Ethical considerations, data privacy, and the need for validation and human oversight remain essential aspects in implementing AI-based solutions in healthcare.
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Tonia Bledsoe, MBA, PMP

I teach organizations how to embrace technology and enable their teams. Certified AI Consultant & Strategist | Corporate Speaker | Sales Consultant | SaaS Implementations | Change Management
In my experience as a patient, AI can significantly improve patient-doctor communication. Beyond generating clinical notes, AI can assist in breaking down complex medical concepts into more understandable language, helping patients grasp their health conditions and treatment plans better. It can also facilitate more frequent communication between visits. For instance, I've often forgotten to mention crucial details during annual check-ups. AI-driven platforms could enable patients to record these thoughts or symptoms as they occur, ensuring they are discussed in subsequent appointments. This not only improves the quality of care but also strengthens the patient-doctor relationship through continuous engagement and understanding
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3 Generate health education materials

A third application of text generation in healthcare is to generate health education materials from keywords, summaries, or questions, such as brochures, articles, or FAQs. This can help health professionals and educators to communicate with patients and the public more effectively and persuasively, and increase their awareness and knowledge of various health topics and issues. Text generation models can be trained on large datasets of health education materials and their corresponding inputs, and use transformers to generate informative and engaging texts that match the tone and style of the target audience. For example, a text generation model can take a keyword input of "diabetes", and produce a brochure that explains what diabetes is, what causes it, what are the symptoms and complications, and how to prevent and manage it.

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Jossy Obi Onwude, MD FAIDH

Product Leader | Clinical Ops| Digital Health | Strategy | Business Development |
Integrating text generation in healthcare transforms patient education. It's about turning complex health data into relatable, easy-to-understand advice. The goal is to move from generic information to personalized guidance. For instance, a user with Crohn's disease gets tailored advice, not just general tips. This makes medical information more like a chat with a knowledgeable friend, enhancing user comprehension and engagement. Continuously testing and refining based on user feedback ensures the advice remains accurate and relevant.
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Abhishek Aman

14k+ | Data Scientist at TCS-Research | 2x Azure GenAI | LLM | NLP | ML | DL | SQL | Python | 1x GCP
We can also generate every information about a particular medicine. 1. What is it allergic too 2. when should we consume 3. Consumption in Pregnancy/not 4. Comparison between few other medicines
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4 Generate research papers

A fourth application of text generation in healthcare is to generate research papers from data, such as tables, graphs, or summaries. This can help researchers and scientists to disseminate their findings and insights more quickly and widely, and enhance the quality and impact of their publications. Text generation models can be trained on large datasets of research papers and their corresponding data, and use transformers to generate well-structured and well-written texts that follow the academic standards and conventions. For example, a text generation model can take a table input of the results of a clinical trial, and produce a research paper that introduces the background, methods, results, and discussion of the trial.

Text generation is a powerful and promising tool that can improve healthcare in many ways. By using text generation models based on transformers, you can generate texts from various data sources that can help you with tasks such as medical reporting, clinical documentation, health education, and research publication. However, text generation also poses some challenges and risks, such as ethical, legal, and social implications, data quality and security, and human oversight and evaluation. Therefore, you should always use text generation with caution and responsibility, and follow the best practices and guidelines of your field and domain.

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Carl Holden

𝗧𝗼𝗽 1% 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗔𝗜 𝗩𝗼𝗶𝗰𝗲, AI Business Strategist, 15+ Years in Marketing as Agency Owner, Author and Speaker
Is it a research paper if a computer does it? I believe research should stay a human endeavor as research and the scientific method require a level of purpose and nuance that computers have no concept of. Why generate a paper if your data is all available in an LLM? Instead, I believe that research papers will be written by the data accumulated, simulated, and processed in an LLM, like how current live tests are done in labs. This is a much better use of our time generally than having computers spit out large generalized papers.
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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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James V Baber

AI Product & Technology Leadership
I would be very careful using Generative AI to generate text in healthcare. There are myriad concerns here that are currently being worked through, one of which is "hallucinations" that require the user to stay vigilant in monitoring whatever text is produced.
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How can you use text generation to improve healthcare?

1

2

3

4

5

1 Generate medical reports

2 Generate clinical notes

3 Generate health education materials

4 Generate research papers

5 Here’s what else to consider

Artificial Intelligence

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How can you use text generation to improve healthcare?

1

2

3

4

5

1 Generate medical reports

2 Generate clinical notes

3 Generate health education materials

4 Generate research papers

5 Here’s what else to consider

Artificial Intelligence

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