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Prompt-based AI (ChatGPT) in Pharma and Healthcare

Prompt-Based AI, also known as ChatGPT, and Machine Learning are two different approaches to artificial intelligence. While both have their own unique applications, they differ in their approach and the types of problems they can solve. In this article we will explore in-depth prompt-based AI and compare the two approaches.

Prompt-based AI, is a type of artificial intelligence that uses natural language processing to generate responses to user input. The algorithm is trained on a large dataset of text data and learns to generate responses based on the input prompt.

One of the main differences from Machine Learning is that ChatGPT lacks expertise in understanding and interpreting tabular data accurately, making it unsuitable for predictions on such data. Also, ChatGPT's sequence-to-sequence architecture, which processes input text sequentially, is not optimized for tabular data. Its training data primarily consists of text from the internet, which may not include enough examples of tabular data or the specific context in which it needs to be used.

Additionally, ChatGPT is designed to understand and generate natural language, not structured data formats like spreadsheets or databases. It lacks the ability to comprehend the row and column structure inherent in tabular data such as relationships between rows and columns, and does not undergo specific training to perform calculations on numerical values and operations like aggregation, filtering and sorting.

Prompt-Based AI (ChatGPT) has a wide range of applications in Healthcare and Life Sciences, including:

Life Sciences

  • Patient screening: used to conduct initial screenings of potential participants for clinical trials. It can ask questions about the patient's medical history, current medications, and any previous participation in clinical trials. Based on the responses, ChatGPT can determine whether the patient meets the eligibility criteria for a specific trial.

  • Informed consent process: assists in the informed consent process by providing patients with information about the trial, its purpose, potential risks and benefits, and the rights and responsibilities of participants. It can answer questions from patients and clarify any doubts they may have, helping them make an informed decision about participating in the trial.

  • Adverse event reporting: used to collect information about adverse events experienced by participants. It can ask questions about the nature and severity of the event, any associated symptoms, and the timeline of occurrence. ChatGPT can then categorize and flag the adverse events for further review by the clinical trial team.

  • Data collection and analysis: gathers data from participants through surveys or questionnaires that monitor trial progress. It can ask questions about their compliance with study procedures, medication adherence, and any changes in their health status. Additionally, it can assist in analyzing the collected data, identifying patterns, and generating insights that can contribute to the overall findings of the clinical trial.


  • Patient triage: used as a virtual assistant to assess the severity of a patient's symptoms and provide appropriate recommendations. For example, it can ask questions about the symptoms, medical history, and other relevant information to determine if urgent medical attention is required or if self-care measures are sufficient.

  • Mental health support: provides emotional support and guidance for individuals dealing with mental health issues. It can engage in conversations, offer coping strategies, and provide resources for seeking professional help when needed.

  • Language interpretation and translation: assists in overcoming language barriers by providing real-time translation services. This can facilitate communication between healthcare providers and patients who speak different languages, improving the quality of care.

  • Chronic disease management: helps patients manage chronic conditions by providing reminders for medication adherence, suggesting lifestyle modifications, and answering questions related to their disease. It can offer ongoing support and motivation for individuals living with conditions such as diabetes, hypertension, or asthma.

  • Post-operative care: guides patients through the post-operative recovery process, providing instructions on wound care, pain management, and potential complications to watch for. It can address common concerns and offer reassurance during the healing period.

In summary, machine learning and prompt-based AI are two different approaches to artificial intelligence, each with its own unique applications. Machine learning is used for prediction and classification tasks (learn more in our prior blog), while prompt-based AI is used for generating responses to user input. By understanding the differences between these two approaches, we can better understand how and when to apply each to solve different sets of problems.

At Equilibrium Point, we bring real-world experience and deep expertise in healthcare data and AI solutions to provide unprecedented business and clinical insights, new levels of patient engagement and improved patient outcomes.

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