THE FUTURE OF HEALTHCARE: ROLE OF AI IN CLINICAL TRIALS

Artificial intelligence (AI) is a field of science and technology focused on developing intelligent computer programs, tools and machines that can perform tasks requiring human-like intellect. It combines insights from various fields, including computer science, engineering, psychology, linguistics and mathematics. Since its inception in 1956, AI has brought significant value to businesses worldwide. Although AI is currently limited to a few operations, it is expected to disrupt multiple industries significantly in the next decade. The healthcare sector has been highly proactive in implementing AI-based technologies, particularly in decision-making and risk assessment processes associated with clinical diagnosis. Furthermore, AI is expected to transform the pharmaceutical and medical industries, with an estimated growth of 40% by 2024. AI is the ability of a system to think like humans, discover the meaning, and execute the tasks. The term was coined in 1955 by John McCarthy and has gained wide popularity till date. Over the years, AI-driven technologies have continued to evolve rapidly with several industries increasingly deploying such solutions across key aspects of value chain. The global AI in clinical trials market is anticipated to grow at a CAGR of around 16%, till 2035, according to Roots Analysis. Driven by the rising number of clinical trials across various regions, the emergence of artificial intelligence in clinical trials is anticipated to grow at exponential rate in the coming years.
Beylikdüzü’ndeki seksi kadınlar beylikdüzü escort web sayfasında kendilerini tanıtıyorlar.
AI IN CLINICAL TRIALS OVERVIEW

Over the years, pharmaceutical R&D expenditure has increased substantially, however, the approval rate has not increased considerably. Developing a successful drug typically takes 10-15 years and costs around USD 1 billion, but despite this significant investment, 90% of drug candidates fail in clinical trials. Major reasons for the failure are poor data management, noncompliance with regulations, and failure to meet primary and secondary endpoints. In addition, patient recruitment delays and non-adherence to treatment also hinder the progress of clinical trials. Emerging technologies, such as AI, cloud computing, and big data analytics, are being explored to transform clinical research. Further, digital technologies, such as mobile sensors and virtual platforms, make it easier to collect detailed patient data and design better trials. Moreover, AI and advanced data analytics enable efficient identification of promising subjects and risk-based monitoring of trial performance in real-time.

APPLICATIONS OF AI IN CLINICAL TRIALS

AI can greatly benefit clinical trials by offering a valuable clinical intelligence solution that can enhance operational efficiency and improve oversight for greater risk mitigation. The applications of AI in clinical trials include:

• Medical Data Analysis: AI has the potential to assist with predicting patient outcomes, offering valuable insights into treatment efficacy and assisting in clinical trial decision-making. Further, it can also automate the data collection and management process, which can minimize errors and enhance the speed of data analysis.

• Medical Imaging: AI can non-invasively visualize and measure disease progression, along with the treatment response through medical imaging. It can identify early signs of cancer and other medical conditions as accurately as human radiologists.

• Site Selection: AI can increasingly be utilized in clinical trials, allowing the researchers to identify the most suitable locations for conducting clinical trials. Moreover, it can analyze a range of factors, including patient demographics, disease prevalence, and healthcare infrastructure, to determine the best sites for conducting the trials.

• Clinical Trial Design: AI-based platforms can successfully design clinical trials by streamlining the process of developing and testing new drugs. In addition, by analyzing vast amounts of data from previous trials, it can identify trends and patterns that may inform the design of future studies. As a result, AI can be poised to revolutionize the drug development process, potentially leading to faster and more effective treatments for a range of medical conditions.

• Integrated Patient Care: The use of AI has the potential to enhance the management of diseases by enabling the development of digital health solutions that prioritize improving patient experiences and outcomes. By analyzing patient data, AI can generate alerts and recommendations that promote proper care actions, leading to better patient engagement and ultimately improved treatment outcomes.

CHALLENGES ASSOCIATED WITH THE ADOPTION OF AI

The challenges restricting the use of AI in healthcare domain are highlighted below:

• Scarcity of Specialists: AI is a relatively new technology, there are a limited number of AI experts and data scientists with in-depth knowledge of healthcare and pharmaceuticals. As per estimates, the demand for such professionals currently exceeds the available supply by millions, with only 300,000 skilled AI professionals worldwide. In order to ensure an adequate supply of skilled AI specialists, several technology giants are investing in initiatives to establish international facilities dedicated to AI development.

• Difficulty in Implementing: Efficient deployment of AI in business processes requires a well-defined implementation strategy for such platforms. However, implementing AI in clinical studies presents a major challenge due to the unavailability of structured healthcare data that can be readily processed by machine learning algorithms.

• Regulatory Issues: Although data privacy is paramount in every industry, it needs to be enforced rigorously in the healthcare domain to ensure protection of confidential medical data. It is worth mentioning that Europe has implemented strict regulations, particularly through the General Data Protection Regulation (GDPR) directives, to protect confidential medical data in the healthcare industry. These guidelines also apply to AI-based solutions, which must obtain CE marking and classification according to the medical device directive. This process involves defining the intended use of the product, identifying the medical device class, submitting a quality management system brief, technical file and clinical evaluation report. Furthermore, such stringent regulations are believed to discourage clinicians and healthcare providers from implementing AI-powered systems in their existing processes. However, in June 2019, Food and Drug Administration (FDA) initiated the development of a modern regulatory framework to ensure availability of safe and effective AI-powered devices to patients.

• Transparency Issues: Transparency in the decision-making process is crucial in medical affairs. The doctor must have a thorough understanding of any suggestion recommended by the AI specialist. This necessitates the development of transparent prediction-explanation tools. With the implementation of advanced AI techniques, it has become increasingly important to strike a right balance between predictive accuracy and model transparency.

FUTURE PERSPECTIVE

Artificial intelligence has demonstrated immense potential in transforming the healthcare industry by revolutionizing drug discovery, clinical trials, treatment, diagnosis, and development, leading to significant improvements in patient outcomes. Currently, it is being used to optimize clinical trial processes and mitigate challenges, such as poor patient recruitment, engagement, monitoring, and study design. In addition, advanced speech and text recognition systems enable effective remote physician-patient communication. Further, predictive AI models can aggregate and analyze large volumes of data for future therapy development. Nevertheless, the integration of clinical datasets with regulatory authorities and their databases is crucial for the widespread application of AI in healthcare. It is worth mentioning that over the years, AI-based technologies have been explored to improve the efficiency of clinical trials for different biologics, with significant funding and partnership activity in this domain. Therefore, in the coming years’ AI-based solutions are expected to strengthen. Additionally, with consistent innovation in the field, a significant improvement during clinical trials is anticipated by making the trials more efficient, cost-effective and patient-centric. Moreover, machine learning, natural language processing, deep learning and data science are expected to simplify complex and time-consuming clinical studies, making them more structured and convenient. The growing demand for accurate and effective AI-based platforms, driven by the benefits of AI in clinical trials and ongoing advancements, is expected to continue in the coming years as biopharmaceutical manufacturers and researchers strive for safe and efficient drug and biologics development.

About Roots Analysis

Roots Analysis is a global leader in the pharma / biotech market research. Having worked with over 750 clients worldwide, including Fortune 500 companies, start-ups, academia, venture capitalists and strategic investors for more than a decade, we offer a highly analytical / data-driven perspective to a network of over 450,000 senior industry stakeholders looking for credible market insights. All reports provided by us are structured in a way that enables the reader to develop a thorough perspective on the given subject. Apart from writing reports on identified areas, we provide bespoke research / consulting services dedicated to serve our clients in the best possible way.