Generative AI for healthcare
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Photo by National Cancer Institute on Unsplash
Generative AI has the potential to transform the healthcare industry, improving patient experiences while taking the strain off the governments, companies and people who deliver increasingly complex and expensive services.
In the following blog, we will take you through examples of areas in which AI is already making a significant positive impact across healthcare, through patient interactions, the pharmaceutical industry and education.
Crucially, the AI tools we describe below are designed to be used responsibly by qualified medical practitioners, with the doctor retaining both control and responsibility for decisions and outcomes affecting patients, in human-in-the-loop type approaches.
AI technologies are developing at incredible pace but that means some of the latest generation of AI products have, as yet, been insufficiently tested in medical contexts and require both further testing and very careful implementation, building confidence and trust of both medical practitioners and patients in how they will be used.
Patient care
Patient notes
Compiling notes on patients’ symptoms, experiences and conditions is an essential but time-consuming task for medical practitioners — but AI can help in multiple ways to streamline the process.
Conversations with patients can be seamlessly transcribed and automatically turned into comprehensive and organised notes, by combining audio and text natural language processing (NLP) tools.
Meanwhile, large-language models (LLMs) can be trained to:
- recognise and highlight key information, like patient names, dates, medical conditions and medications;
- extract timelines of symptoms, treatment progress and changes in a patient’s condition;
- tag and categorise patient notes based on their content;
- identify sentiments expressed in patient notes, so healthcare providers can better understand the emotional state of their patients; and
- redact and anonymise patient notes.
Enhancing diagnoses
LLMs help to shorten the connections between the huge wealth of medical knowledge and the practitioners and patients who need it. They contribute to clinical decision support by analysing huge quantities of healthcare data, interpreting health records, and extracting valuable insights, for example a more accurate cancer risk profile. While this does not replace the need for the expertise of medical practitioners, LLMs can provide a valuable time-saving tool by helping to connect them with the information they need.
LLMs can be trained to stay updated on the latest research and summarise complex medical literature, to generate recommendations tailored to each patient — taking into account anything unusual or anomalous in their case.
Personal assistance
Both healthcare staff and patients can benefit from personal, virtual assistants with the support of Generative AI tools.
Practitioners
Practitioners can be relieved from parts of their daily admin — like managing repeat prescriptions, status updates, and report writing — giving them more time to focus on their patients’ needs. AI-powered virtual assistants can also intelligently allocate resources, manage the availability of emergency rooms and beds, and identify critical situations early.
LLMs are perfect for analysing datasets, like those related to specific surgical procedures and patient recovery paths, and can use these to help the physician develop personalised post-surgery plans.
The same applies to providing tailored mental health plans. Through analysis of a patient’s mental health records and their response to previous interventions, LLMs can contribute towards crafting highly personalised support plans.
Patients
Generative AI can also help make support more accessible to patients: by simplifying technical language, translating communications, and answering questions about their healthcare.
Trained to understand the context in which they operate as a virtual healthcare assistant, LLMs can take care of routine matters quickly for patients, while delegating to healthcare professionals when required.
Read more https://medium.com/mantisnlp/applications-of-llms-in-the-pharmaceutical-industry-b90cfce161da
Pharmaceutical industry
Drug discovery
LLMs are now indispensable in drug discovery, revolutionising the way researchers approach the identification and development of custom medications.
As models like GPT-4 excel in understanding human-like text, they can quickly and accurately digest scientific literature, research papers and data. Traditional retrieval systems, like those on PubMed, suffer from inconsistent tagging and limitations in accurately returning information. Advanced retrieval systems are increasingly applying NLP, allowing users to ask questions using natural language, which improves accuracy and flexibility, and allows for more context to be included than with keyword models.
Researchers can also use AI to assist in the design of drug molecules, by exploring virtual chemical spaces using natural language prompts. At the same time AlphaFold already plays a pivotal role in predicting the physicochemical characteristics of potential drug candidates in order to optimise molecular structures for enhanced bioavailability and efficacy.
Clinical trials
With the ability to provide a nuanced understanding of real-world scenarios and data, LLMs identify patterns and risk factors in historical real-world evidence, making them ideal for planning better-informed clinical trials with optimal study endpoints.
Once a trial is developed, the prompt enrollment of patients is essential to avoid delays or premature termination of trials — for this, NLP can help by querying electronic health records, helping to identify and sign up eligible participants.
Regulatory compliance
When it comes to regulatory compliance and documentation in drug development, LLMs have an instrumental role to play in streamlining the process. By automating the extraction and summarisation of data to provide comprehensive evidence from real-world sources, they can smooth the process of ensuring regulatory submissions are backed by well-organised and relevant evidence, and help get them more quickly written and formatted the way regulators require.
LLMs also analyse and interpret regulatory updates to ensure pharmaceutical companies are abreast of the latest guidelines. This proactive approach ensures firms fulfil their legal requirements to adhere to regulatory standards, while putting patient safety and public trust front and centre.
Read more https://medium.com/mantisnlp/applications-of-llms-in-the-pharmaceutical-industry-b90cfce161da
Education
Content creation
LLMs are a versatile tool for all kinds of educators, and healthcare is no exception. They can be used to help generate compelling and accessible content, including summaries, presentations, translations and contextualised explanations, across the full spectrum of medical topics.
With the ability to adapt the tone and delivery of content to individual learners’ needs, LLMs open up new possibilities for creating engaging revision aids, test examples and study materials, all tailored to users’ needs. By taking into account learners’ interests, skill gaps, and learning history, this increases the relevance of, and engagement with, the materials provided.
Virtual environments
The development of virtual environments in which students can interact with simulated patients is a revolution in healthcare education.
LLMs can also play a useful role as a partner to virtual reality (VR) in medical education, creating more immersive learning experiences. Using the two technologies together, educators can design courses which are more realistic, interactive and customised, putting learners into lifelike medical scenarios and helping them hone their decision-making abilities within a risk-free environment.
Feedback and evaluation
AI-powered adaptive tests, which adjust question difficulty in real time according to the candidate’s performance, can provide a more accurate evaluation of knowledge and skills than traditional, one-size-fits-all exams. Plus, feedback can be provided instantly, with AI systems grading answers and offering relevant educational support to learners.
Finally, AI can take care of the bigger picture too, providing analytics to teachers and institutions on their students’ progress, engagement and outcomes, to help them make informed, data-led decisions about their teaching.
Read more https://medium.com/mantisnlp/applications-of-llms-in-educational-healthcare-7abd39428343
Conclusion
Generative AI has an ever-growing role to play in healthcare, and brings the opportunity to transform the level of care patients receive.
To find out more about how Mantis can help you integrate NLP and AI solutions into your healthcare business,
Generative AI for healthcare was originally published in MantisNLP on Medium, where people are continuing the conversation by highlighting and responding to this story.