A Fireside Chat with ChatGPT

February 2, 2023


mins read

Have you ever sat back and thought to yourself - hey, what does AI think of clinical trials? Well, if you have, then we have something in common!

Unless you’ve gone off the grid as part of your New Year’s resolution, you’ve probably heard of a little something called ChatGPT. The potential applications of this nifty chatbot are endless, but we thought we’d see what it thinks about clinical trials.

We hope you enjoy this edition of a Fireside Chat (it counts if the fire is a YouTube video playing in the background, right?)

What are the main pitfalls with clinical trials at the present moment?

For a robot, the answers weren’t half bad, and they do shed light on a lot of the problems people running trials face. Lack of diversity is a major issue when it comes to clinical trials. The FDA’s snapshot analysis from 2020 looking at just under 300,000 trial participants found 11% and 7% of trial participants were of Asian and Black/African-American backgrounds respectively. Stats like that show we aren’t quite there when it comes to providing data that serves a global population.[i]

  1. Lack of diversity in study participants: Clinical trials often fail to recruit a diverse population, which can limit the generalizability of the results.
  2. High cost: Clinical trials can be expensive, and the cost can be a barrier for some companies and researchers.
  3. Time-consuming: Clinical trials can take a long time to complete, which can delay the development and approval of new treatments.
  4. Complex regulations: There are many regulations that must be followed during a clinical trial, which can be difficult to navigate.
  5. Risk of bias: Clinical trials are subject to various types of bias, such as selection bias and publication bias, which can affect the validity of the results.
  6. Adverse events: Clinical trials also come with a risk of adverse events and side effects, which can occur when patients are exposed to new treatments.
  7. Data security and privacy: The protection of patient data is a vital concern in clinical trials, and strict regulations must be followed to ensure data security and privacy.

How can we bolster diversity in clinical trial recruitment?

To hone in on the diversity issue a little more, we were curious what ChatGPTs neural network had to say about solving the problem it highlighted. The proposition of having a diverse research team was actually super interesting – is there perhaps an element of unconscious bias that plays in recruitment? Does having a more diverse recruitment team help reduce the impact of that? Interestingly, Niranjan et al conducted a qualitative study regarding perspectives on recruiting minorities to oncological trials, and there is evidence that there is some negative perception in recruitment towards minorities.[ii] This does raise the possibility that introducing AI into clinical trial recruitment might help mitigate the presence of this bias.

  1. Outreach and education: Providing education and information about clinical trials to communities that are traditionally underrepresented in clinical research can help increase awareness and participation.
  2. Community engagement: Building relationships with community leaders and organizations can help to build trust and increase participation in clinical trials.
  3. Targeted recruitment: Strategies such as targeted advertising, community-based participatory research, and culturally-tailored recruitment materials can help to increase participation among underrepresented groups.
  4. Waiving certain inclusion criteria: For example, waiving a requirement for a certain level of education or income can help to increase participation among people who might otherwise be excluded.
  5. Incentives: Offering incentives such as transportation assistance or childcare can help to reduce barriers to participation.
  6. Diversifying research team: Having a research team that is diverse can help to increase the diversity of trial participants.
  7. Reviewing and monitoring the data: Regularly monitoring the diversity of trial participants can help identify areas where recruitment efforts need to be improved and help to track progress over time.
  8. Addressing the systemic issues: Addressing the systemic issues that contribute to disparities in health outcomes is also crucial for increasing diversity in clinical trials.‍

Do you think machine learning could be leveraged to tackle things like how time-consuming clinical trials can be?

We’re probably most excited for the first point ChatGPT raised which is the potential applications of predictive modeling. It isn’t just predicting outcomes, but it could even be used to predict things like trial termination.

This question was totally not put in as an opportunity for us to talk about how we recently got published in … Hey by the way, we got published in Nature for demonstrating machine learning can be used to predict early trial termination and further optimise clinical trial design and execution.[iii]

Yes, machine learning (ML) has the potential to significantly reduce the time and cost of clinical trials by automating certain tasks and reducing the need for manual labor. Some examples of how ML could be leveraged in clinical trials include:

  1. Predictive modeling: ML algorithms can be used to predict patient outcomes and identify those most likely to respond to a particular treatment, allowing for more efficient trial design and patient selection.
  2. Data management: ML can be used to automate data entry, cleaning, and analysis, reducing the time and labor required to manage large amounts of data.
  3. Adverse event detection: ML can be used to automatically detect and report adverse events, reducing the time and resources required for manual monitoring and reporting.
  4. Trial monitoring: ML can be used to monitor trial data in real-time, allowing for more efficient and effective trial monitoring and reducing the risk of errors.
  5. Clinical decision support: ML can be used to develop clinical decision support systems that can assist physicians in making treatment decisions based on a patient's specific condition and characteristics.

It's important to note that ML is an evolving field, and it's important to evaluate and validate the results of any ML-based applications in clinical trials. Furthermore, it's essential to ensure that any ML applications used in clinical trials are accurate, reliable, and have undergone appropriate testing and validation.

What do you think clinical trials will look like in 100 years?

Alright ChatGPT, show us your crystal ball. In fairness to the bot, a century is a long time and technology does change exponentially – it’s hard to predict something when the machine hasn’t had a chance to learn what 2123 looks like, but we thought we’d find out anyway.

It is difficult to predict exactly what clinical trials will look like in 100 years, as advancements in technology and medicine are constantly evolving. However, it is likely that clinical trials will continue to become more personalized and efficient. Some possible trends include:

  1. Increased use of AI and machine learning: AI and machine learning technologies are likely to play an even larger role in clinical trials, from identifying patient populations most likely to respond to a particular treatment to automating data management and analysis.
  2. Personalized medicine: Clinical trials will likely become more tailored to individual patients, with treatments and therapies designed to target specific genetic, molecular, or other patient-specific characteristics.
  3. Remote monitoring: With advances in technology, it's possible that clinical trials will become more virtual, with the use of remote monitoring and data collection to minimize the burden of trial participation.
  4. Big data analysis: With the increasing amount of data generated by electronic health records (EHRs), genomics, and other sources, clinical trials will likely leverage big data analysis to gain new insights and improve trial design.
  5. Greater patient engagement: Patient engagement will likely play an even greater role in clinical trials, with patients being involved in the design, conduct and dissemination of trial results.
  6. Use of virtual and augmented reality: Virtual and augmented reality technologies are likely to be used in clinical trials to enhance the patient experience, train physicians, and improve data visualization.
  7. Increasingly complex and personalized research: As we learn more about the underlying biology of diseases and the individual variability of patients, the research will become more complex and personalized.
  8. Greater transparency and dissemination of information: With the increasing use of technology and data, there will be more transparency in the process of clinical trials and more accessible information for the public.

It's important to note that these predictions are based on current trends, and the field of medicine and technology are constantly evolving, so it's possible that the future of clinical trials will take a different direction.

If you had one piece of advice for someone, whether they have extensive experience or no experience at all, to run a clinical trial, what would you advise?

If you ask us, you could distil the below advice from ChatGPT to the following sentence; work with an experienced team. Naturally, clinical trials are complex processes to navigate regardless of how much experience someone has. Working with a company that understands how to seamlessly combine expertise in clinical trials with cutting-edge technology is probably the best way to go about a clinical trial.

My advice for someone who is planning to run a clinical trial, whether they have extensive experience or no experience at all, would be to thoroughly plan and organize the trial in advance. A well-planned and organized trial is more likely to be successful and will minimize the risk of errors and delays.

  1. Develop a clear and detailed protocol: The protocol should outline the objectives, design, and methodology of the trial, as well as the inclusion and exclusion criteria for participants.
  2. Get the right team: Building a team with the necessary expertise and experience is crucial for a successful trial. This includes recruiting experienced investigators, research coordinators, and data managers.
  3. Obtain necessary approvals and funding: Before starting the trial, make sure that you have obtained all necessary approvals from regulatory bodies and that you have secured adequate funding for the trial.
  4. Recruit the right participants: Carefully select participants who meet the inclusion criteria and who are likely to complete the trial. Also, pay attention to the diversity of the population you are recruiting.5
  5. Monitor the trial closely: Regularly monitor the progress of the trial and make sure that all data is collected, recorded, and reported accurately. This will help to ensure that the trial is conducted safely and ethically.
  6. Prepare for the unexpected: Be prepared for unexpected challenges and complications, and have a plan in place for dealing with them.
  7. Communicate effectively: Keep all stakeholders, including participants, investigators, and sponsors, informed about the progress of the trial and any issues that arise.
  8. Evaluate and disseminate the results: Once the trial is complete, analyze the data and disseminate the results to the appropriate audiences.9

Remember, running a clinical trial is a complex and challenging process, but with proper planning and organization, it can be done successfully

Firstly, this is incredibly exciting technology and we’re amazed at what everyone at OpenAI is doing – this technology applied in the right way is going to be transformational for the way we work. If AI can write you a PhD dissertation these days, it’s not long until we start seeing ground-breaking stuff in the clinical trial space. Secondly, you’ve all the arguments for how AI is streamlining the way clinical trials are run – it’s important to work with people who understand the importance of continuing to develop and optimise trial design. And finally, that last question to ChatGPT was an important one because it shows that even machine learning recognises the importance of working with a team with solid expertise in clinical trials (hint hint: get in touch!).

Thanks for joining us for this slightly more virtual than usual Fireside Chat – we hope you found this as enjoyable as we did! What else would you want to know from ChatGPT? Drop us an e-mail and let us know.

Our mission at Lindus Health is to accelerate clinical trials, so patients can benefit from new treatments sooner. We are a next-generation CRO helping Digital Therapeutics pioneers run radically faster, more reliable clinical trials. We do this by marrying a world class clinical team with an end to end technology platform, including EDC, database, site and feasibility products. We also have unique access to over 5m electronic health records. We can recruit participants and execute a trial efficiently, from capturing trial endpoints, managing screening and patient visits.

Our products have been used to recruit, onboard and handle data from thousands of patients and Lindus Health has helped run over 70 studies up to 3x faster than traditional CROs. Get in touch to see how we could help solve your next clinical trial!

[i] https://www.fda.gov/media/143592/download

[ii] https://acsjournals.onlinelibrary.wiley.com/doi/full/10.1002/cncr.32755

[iii] https://www.nature.com/articles/s41598-023-27416-7

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