AI IN CLINICAL TRIALS
Margarita Ann
Created on April 25, 2023
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Transcript
BY: ELVIA ANNA RIJU, VEDA PALLIPURTH AND MARGARITA ANN
PRESENTATION
A.I IN CLINICAL TRIALS
14. Open-ended Question
7. Phases
13. Conclusion
6. End of Journey
12. Video
5. Participation
11. A.I. Disadvantages
4. The Journey
10. A.I. Advantages
3. Aims
9. Key Data limits
2. Introduction
8. A.I Implemts
1. Summary
INDEX
Clinical trials are research studies that test a medical, surgical, or behavioral intervention in people. These trials are the primary way that researchers determine if a new form of treatment or prevention, such as a new drug, diet, or medical device (for example, a pacemaker), is safe and effective in people. Both and now, A.I has a wide range of possible uses in Clinical Trials. With the power of A.I, companies can rapidly digitilize Clinical trial proccesses to increase efficiency and accuracy.
SUMMARY
What are Clinical Trials?
AN introduction
Clinical Trials
- Clinical research is the study of health and illness in people. There are two main types of clinical research: observational studies and clinical trials
- Observational studies monitor people in normal settings. Researchers gather information from people and compare changes over time.
- Clinical trials are research studies that test a medical, surgical, or behavioral intervention in people. These trials are the primary way that researchers determine if a new form of treatment or prevention, such as a new drug, diet, or medical device (for example, a pacemaker), is safe and effective in people.
- Often, a clinical trial is designed to learn if a new treatment is more effective or has less harmful side effects than existing treatments.
AIMS OF CLINICAL TRIALS
- Testing ways to diagnose a disease early, sometimes before there are symptoms
- Finding approaches to prevent a health problem, including in people who are healthy but at increased risk of developing a disease
- Improving quality of life for people living with a life-threatening disease or chronic health problem
- Studying the role of caregivers or support groups
CLINICAL TRIAL JOURNEY
Why participate in them?
People volunteer for clinical trials and studies for a variety of reasons, including:
- They want to contribute to discovering health information that may help others in the future.
- Participating in a clinical trial helps them feel like they are playing a more active role in their own health care.
- The treatments they have tried for their health problem did not work or there is no treatment for their health problem.
What happens when a clinical trial or study ends?
- Once a clinical trial or study ends, the researchers analyze the data to determine what the findings mean and to plan the next steps.
- As a participant, you should be provided information before the study starts about how long it will last, whether you will continue receiving the treatment after the trial ends (if applicable), and how the results of the research will be shared.
Clinical trials of drugs and medical devices advance through several phases to test safety, determine effectiveness, and identify any side effects. The FDA typically requires Phase 1, 2, and 3 trials to be conducted to determine if the drug or device can be approved for further use. If researchers find the intervention to be safe and effective after the first three phases, the FDA approves it for clinical use and continues to monitor its effects.
PHASE 01
trial tests an experimental drug or device on a small group of people (around 20 to 80) to judge its safety, including any side effects, and to test the amount (dosage)
PHASE 03
trial gathers additional information from several hundred to a few thousand people about safety and effectiveness, studying different populations and different dosages etc. If the FDA agrees that the trial results support the intervention’s use for a particular health condition, it will approve the experimental drug or device
PHASE 04
PHASE 02
trial takes place after the FDA approves the drug or device. The treatment’s effectiveness and safety are monitored in large, diverse populations. Sometimes, side effects may not become clear until more people have used the drug or device over a longer period of time.
trial includes more people (around 100 to 300) to help determine whether a drug is effective. This phase aims to obtain preliminary data on whether the drug or device works in people who have a certain disease or condition. These trials also continue to examine safety, including short-term side effects
PHASES
A.I IN CLINICAL TRIALS
With the power of AI, companies can rapidly digitize clinical-trial processes so they can complete studies faster. That means life-saving medicines and treatments can get to patients more quickly—and life sciences companies could gain a competitive edge. In fact, according to Deloitte’s life sciences digital innovation survey, 76% of respondents are currently investing in AI for clinical development.
Key data limitations
Despite the lightning speed at which COVID-19 vaccines were developed, research from the Deloitte Centre for Health Solutions suggests that it often takes 10 to 12 years to bring a new drug to market. The clinical-trial phase averages five to seven years. This timeline is due to the traditional flow of data across the clinical-trial life cycle, which can be a complicated maze of manual effort, rework, and inefficiency. Consider these key data-related limitations of the traditional clinical trials process:
- Fragmented data and disconnected systems: Inputs for trial artifacts are often scattered across dozens of systems and formats.
- Extensive manual effort: Artifact creation requires manual data transcription from documents and systems.
- Rework and repetition: Although trials typically reuse data components, the same work is often repeated across trials. In the words of one executive, “Databases are still being built from the ground up for most trials. We end up building the same database 400 times.”
- Challenges in enabling innovative trial models: Complexities and limitations related to integrating data from new sources can create challenges with virtual trial designs.
- Thankfully, AI can help CIOs overcome these challenges. AI technologies can be used to create structured, standardized, and digital data elements from a range of inputs and sources.
- For example, CIOs can implement new AI-powered tools to automate data management across the trial lifecycle. These tools intelligently interpret data elements, feed downstream systems, and auto-populate required reports and analyses.
- These tools can leverage existing systems to seamlessly integrate the data flow—providing a single, collaborative touch point for all interactions during a clinical trial. They can even use AI to generate insights from past and current trials to inform and improve future trials
The A.I Advantage
With AI, life sciences and health care organizations can likely gain significant benefits—both in collecting trial data and promoting digital data flow:
- Tapping more participants and more diverse populations
- Boosting participant retention
- Producing faster trials at lower cost
- Increasing reusable data
- accelerate the drug development process and help companies get new treatments to market more quickly
The A.I Disadvantage
1. Date security and privacy.2. Medical record data comes in different formats3. inherent bias
VIDEO
Thank You!