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The need for new drugs and medical treatment has been greater than ever. However, drug development is a complex and time-consuming process. Despite the lightning speed at which COVID-19 vaccines were developed, it often takes 10 to 12 years to bring a new drug to market, and the clinical trial phase averages five to seven years.
Even reaching the trial phase gives no guarantee that the drug will get the US Food and Drug Administration (FDA) approval, as the vast majority of R&D efforts fail to produce a market-worthy product, and only 12% of such drugs receive FDA approval.
So, to come up with a breakthrough drug, Pharma companies need to leverage AI capabilities that can reliably enhance the FDA approval rate while ensuring drug effectiveness and safety.
Let’s discover more about the various use cases, benefits, and limitations of using artificial intelligence in clinical trials.
Artificial intelligence (AI) in healthcare is becoming increasingly prevalent across the industry. According to Statista, the global healthcare AI market was worth around $11 billion in 2021 and is projected to be worth $188 billion by 2030, increasing at a CAGR of 37% from 2022 to 2030.
AI is set to be the most disruptive technology in drug development, enabling automation, unlocking advanced analytics, and increasing speed across the phases of the clinical trial.
Today’s clinical trials value chain is shaped by macro trends that include climate pressure, geopolitical uncertainty, and the COVID-19 pandemic. Furthermore, the increasing demand for personalized treatment and advancements in adaptive design have made clinical trials more complex than ever. AI offers optimization opportunities across every aspect of the clinical trial process, including data analysis, pattern recognition, and early identification of potential problems.
Also Read: How AI Expedites Medical Diagnosis?
Artificial Intelligence offers various valuable use cases in clinical trials, redefining the way research and development processes are conducted in the healthcare industry. The use of AI in clinical trials can revolutionize the entire drug development process, enabling more efficient data management, improved decision-making, and overall success of the clinical trial value chain.
Here are some of the most prominent use cases of AI in clinical trials:
Artificial intelligence in clinical trials helps review and analyze regulatory documents, such as Investigational New Drug (IND) applications. It helps identify errors, inconsistencies, or missing information, ensuring compliance with regulatory standards and accelerating the submission process.
The use of AI in clinical trials starts from the initial stages, where it transforms the way study protocols are designed. By analyzing historical data, the technology suggests protocol improvements, defines endpoints, and recommends patient recruitment criteria, leading to more efficient and scientifically robust trials.
Artificial intelligence in clinical trials analyzes patient data, electronic health records (EHR), and medical literature to match eligible patients with specific trial criteria. While selecting the patients for clinical trials, AI assesses various factors, including geographical locations, patient demographics, and site performance history. This speeds up patient recruitment and ensures a more precise selection process.
AI continuously monitors clinical trial data for safety signals and adverse events. By analyzing patient data in real-time, AI can promptly identify potential safety concerns, allowing for immediate actions to protect patient safety and ensure regulatory compliance.
One of the most groundbreaking applications of AI in clinical trials is the idea of digital twins. Artificial intelligence in clinical trials can create virtual replicas of patients based on their genetic, medical history, and ongoing health data. These virtual replicas serve as dynamic models that simulate and predict outcomes, ushering in a new age where healthcare is truly safe, effective, and individualized.
Since AI can develop predictive models based on patient characteristics and biomarkers, it helps researchers assess how a particular patient responds to various interventions, optimizing treatment efficiency and reducing risks. This approach can potentially transform personalized medicine, detecting potential issues at an early stage and tailoring therapies to each patient’s unique condition.
Using AI for clinical trials offers several advantages that help enhance the accuracy, efficiency, safety, speed, and overall success of the drug development process. Mentioned below are some of the many benefits of AI in clinical trials.
One of the most obvious benefits of AI In clinical trials is the automation of labor-intensive and time-consuming tasks with remarkable speed and precision. For example, AI can swiftly analyze vast datasets, match patients to clinical trial criteria, and process complex scientific data, tasks that human researchers would take much longer to perform. As a result, R&D teams can expedite the drug development process, bringing potential treatments to patients more quickly.
By automating various research and development processes, AI can reduce the need for extensive manual labor and repetitive tasks. This leads to cost savings in terms of labor, resources, and operational expenses. Furthermore, AI can identify and prevent inefficiencies in clinical trials, reducing the risk of costly protocol amendments and ensuring that resources are allocated more efficiently.
AI for clinical trials can also assist in maintaining compliance with regulatory standards by providing real-time monitoring, documentation, and audit trails for clinical trial data and processes. It ensures that the R&D team remains aligned with regulatory requirements, minimizing the risk of costly delays or healthcare compliance issues.
The large amount of data generated in clinical trials can be overwhelming. AI can quickly analyze and organize the sheer volume of data and identify patterns that human researchers would take much longer time to spot or might overlook sometimes. Artificial intelligence in clinical data management helps the R&D team quickly access organized data, which saves time on manual data management and reduces the risk of data errors.
Every patient deals with unique needs and complexities, making it challenging to test the treatment efficacy. AI can play a pivotal role in pinpointing particular patient groups that are most likely to benefit from a specific medication based on factors like genetic profiles and lifestyle, making personalized medicine a reality.
Applications of AI in clinical trials also help tailor treatments to patients by identifying biomarkers, predicting treatment responses, and optimizing trial protocols. This patient-centric approach enhances the likelihood of successful outcomes for trial participants. Patients receive treatments that are more likely to be effective for their specific conditions, leading to better clinical responses and quality of life.
These benefits of AI for clinical trials lead to more effective and accurate development of novel drugs for a wide range of diseases.
While there are many benefits of using clinical AI solutions, there are also some limitations that must be addressed to leverage its maximum potential. Let’s discover some of the most common challenges that hamper the way of successful implementation of artificial intelligence in clinical research.
AI in healthcare counts on high-quality data to identify patterns and make the right decisions. If the data is inconsistent, incomplete, or biased, AI will not be able to able to make accurate predictions, leading to inefficiency in the process, potentially insure drugs, and failure to seek FDA approval.
To overcome this challenge, we must prioritize data quality assurance measures, including data cleaning, standardization, and bias mitigation.
Ensuring the privacy and security of patients’ data is another significant challenge. The healthcare and pharmaceutical industry deals with a high volume of sensitive data on a daily basis, posing a threat of unauthorized access and data breaches.
Therefore, when leveraging AI for clinical trial services, we must take proactive measures to ensure that patient data is secure and their privacy is not compromised.
You may like reading: How does blockchain resolve data privacy and security issues?
The use of AI in clinical trials raises questions about patient safety and decision-making accountability as the technology blurs traditional roles and necessitates clear responsibility allocation.
We must ensure that they adhere to regulatory compliance and follow ethical standards, ensuring the safety and well-being of trial participants.
There are several real-world examples of artificial intelligence in clinical trials, improving the various aspects of patient care and the drug development process:
Insilico Medicine’s INS018_055 is one of the best examples of artificial intelligence in clinical trials. Currently, the drug has reached phase 2 trials for the treatment of idiopathic pulmonary fibrosis, a chronic condition that causes scarring in the lungs and creates problems in breathing. Hong Kong-based Insilico Medicine uses next-generation AI systems to connect chemistry, biology, and clinical trial analysis for the successful development of INS018_055.
While there are other AI-designed drugs, INS018_055 is the first drug with both a novel AI-discovered target and a novel AI-generated design. Insilico Medicine has two more drugs in the clinical stage partially generated by AI. One is currently in phase 1 clinical trials for COVID-19, while the other, designed for treating solid tumors, has recently obtained approval from the FDA to commence clinical trials.
Tempus is making precision medicine a reality by applying AI for clinical trials and healthcare, deriving insights from its extensive library of clinical and molecular data. Tempus uses AI to streamline clinical trial recruitment and management. The company analyzes electronic health records and other patient data to identify suitable trial candidates and monitor trial progress, leading to more efficient and cost-effective trials.
Recursion Pharmaceuticals, a clinical-stage TechBio company, employs AI, ML, and computer vision to develop a drug discovery platform and identify potential drug candidates by analyzing cellular images. This approach accelerates the drug discovery process, allowing the company to investigate a broad range of compounds and their effects on diseases.
The future of artificial intelligence in clinical research is promising as the technology is seemingly advancing at breakneck speed, revolutionizing every phase of the clinical trial value chain.
AI plays an increasingly integral role in accelerating drug discovery and development, from optimizing trial protocols and patient recruitment to enhancing data analysis and safety monitoring. With AI’s capacity to drive precision medicine, identify novel therapies, and simulate trial strategies, it promises faster time to market, reduced costs, and more effective, personalized treatments.
As the technology continues to evolve, it will most likely contribute to more efficient, ethical, and successful clinical trials, benefiting patients and the healthcare industry as a whole.
While there are still safety and efficacy concerns with the applications of AI in clinical trials, the hope is that in the future, AI will take on more responsibilities in the drug development process to guarantee speed, accuracy, and efficiency.
People within the industry should use it as a valuable tool while maintaining a balance between innovation and patient safety to ensure the ethical and responsible use of AI for the benefit of all.
In the rapidly evolving landscape of healthcare, leveraging the power of artificial intelligence in clinical trials is no longer an option but a necessity. And Appinventiv can be your trusted tech partner, committed to being your guiding light in the journey of digital transformation in healthcare.
Being a reputed AI development company, we have extensive experience and expertise in assisting medical, pharmaceutical, and biotech companies in maximizing the impact of AI within clinical trials and other phases of healthcare.
For instance, we have developed an innovative mHealth solution for YouCOMM, which redefines in-hospital patient communication by providing patients with real-time access to medical help, improving patient-caregiver communication efficiency.
For another client, Soniphi, we have built the first resonant frequencies-based personal wellness system. The system helps the company bring Human bio-acoustics science to mobile while enabling millions of global users to witness the synergy between a voiceprint and the physiology, personality, structure, and function of the human body.
Our expertise in providing healthcare software development services can also be witnessed through our project – Health-eApp. It is a platform where users can store and monitor all their medical apps, track devices’ data, find caregivers, and connect with people, making the extensive healthcare world a community.
Our proven track record of providing AI development services for various healthcare brands speaks volumes of our competence in delivering custom clinical AI solutions.
By partnering with us, you don’t just leverage the potential of cutting-edge technology but also benefit from a dedicated team of 1200+ software development experts who understand the unique challenges and opportunities within the clinical trial landscape.
So, embrace the future of artificial intelligence in clinical trials with Appinventiv and embark on a more efficient, ethical, accurate, and secure drug development process that will most likely win FDA trust and get approval.
Q. How can AI help in clinical trials?
A. AI can help clinical trials by optimizing patient recruitment, predicting treatment efficacy, automating data analysis, and enhancing safety monitoring. In addition, it accelerates trial processes, reduces costs, improves data quality, and leads to more efficient, personalized, and successful clinical trials.
For instance, AI can quickly analyze the sheer volume of patient data and identify patterns that would otherwise be time-consuming and difficult to detect when done manually. Also, AI monitors patients in real time, helping researchers track their progress and detect potential side effects instantly.
Q. What are examples of artificial intelligence in clinical trials?
A. some of the most common applications and examples of artificial intelligence in clinical trials are patient recruitment and eligibility matching, predictive analytics for trial design, real-time safety monitoring, automated document review for regulatory compliance, and artificial intelligence in clinical data management and analysis. AI is also used for protocol optimization, treatment response prediction, and biomarker discovery, making clinical trials more efficient, accurate, safer, and cost-effective.
Q. What is the future of AI in clinical trials?
A. The future of AI in clinical trials is promising as the global AI in clinical trials market is poised to be worth $4.8 billion by 2027. As the technology continues to evolve, it is more likely to take on more responsibilities in drug development and clinical trial landscape.
With the potential to streamline trial design, enhance patient recruitment, improve data analysis, and contribute to developing more personalized and effective treatments, AI for clinical trials is poised to accelerate drug discovery and reduce costs.
All in all, artificial intelligence in clinical research will play a pivotal role in shaping the future of healthcare by making the process more efficient, ethical, accurate, and successful.