Evolution of AI in Healthcare and Clinical Research

If asked about the origins of artificial intelligence (AI), what comes to mind?

For many, sentient robots or non-human entities from science fiction books and films are the embodiment of AI. Think of Maria in Metropolis (1927), HAL 9000 in 2001: A Space Odyssey (1968), C3PO in Star Wars (1977), and Terminator (1984), to name but a few. For others, especially given the amount of news coverage about large language models (LLMs) at the moment, ChatGPT will immediately spring to mind.

A History of AI

Humankind has been debating whether or not it’s possible to create artificial intelligence since it first contemplated creating automated machinery. Descartes came up with a test to differentiate between human responses and those of automata in the 17th century. In 1950, Alan Turing—widely regarded as the father of computer science and modern AI—developed the "Turing Test" to answer the question “can machines think?”, which later evolved into “can machines imitate humans?”.

“I think, therefore I am.”
– René Descartes, 1596-1650

Today, these questions are still asked, but with wider implications for society as a whole as AI has suddenly become extensively integrated into our lives through search engines, Siri, Alexa, ChatGPT, etc.

The actual term “artificial intelligence” was first coined in 1956 at Dartmouth summer camp by John McCarthy and a group of pioneering computer scientists. Later that year, Logic Theorist—widely regarded as the first AI software to run—was created by Cliff Shaw, Allen Newell, and Herbert Simon to perform automated reasoning. There are other earlier examples, such as a checkers program in 1952, but Logic Theorist is the most commonly referenced.

Since then, AI has evolved alongside the field of computer science, driven by a desire to make computers mimic human decision-making processes. It’s also been by necessity, as technology marches ever forward and requires more powerful tools to help humans manage all that comes with such advancements.

At its core, the definition of what is now thought of as AI is the encompassing of any application where computers are tasked with making decisions and solving problems in ways similar to humans. Self-awareness and sentience are not included in that description; these remain a theoretical science at the moment, and for the foreseeable future. For current AI applications, it’s also true that not all AI is created equal, which can muddy the waters when choosing a solution to business problems.

Initially, AI systems relied on rule-based approaches, akin to a series of if-else statements, known as expert systems. These early systems were groundbreaking at the time but limited by their rigid structure. Systems like this are still very prevalent now.

The true revolution in AI came as a result of advancements in technology, better data-sharing infrastructures, and the exponential growth in data volumes over the last 25 years. This led to complex, data-intensive processes that are unmanageable by humans and standard computational resources—enter machine learning (ML), which has been one of the subsets of AI since the 1950s. 

Unlike rule-based systems, ML algorithms are perfect for processing vast amounts of data and then learning, adapting, and refining their decision-making processes without explicit programming. This allows for flexible and dynamic AI systems that are capable of handling complex real-world problems. 

AI & Clinical Trials

The clinical trials ecosystem is an ideal environment to reap the benefits of these AI systems and the results have been transformative. Biovia, part of the Dassault Systèmes Group, has been a visionary leader in this field, supporting life sciences since 2001. Medidata began integrating elements of AI into our platform many years before the current wave of interest in the technology. These technologies are leveraged as powerful tools—human-assisted or human-reviewed in many cases—to solve specific challenges that cannot be addressed through traditional methods.

The process of integrating AI and ML into clinical research begins with identifying gaps or needs where existing solutions are inadequate. Once a problem is identified, researchers leverage historical data and existing systems to develop AI-driven solutions carefully. 

Central to the success of AI in clinical research is the careful curation of data and the development of robust ML models. This is a critically important task and one in which these models excel. Rather than relying solely on human intuition or rule-based logic, ML models learn from vast datasets, extracting patterns and insights that may elude human analysis. This symbiotic relationship between human expertise and ML algorithms is essential for driving innovation and improving patient outcomes.

In practice, AI and ML are integrated across the entire lifecycle of clinical research, from study design and patient recruitment to data analysis and interpretation. These technologies let researchers extract valuable insights from complex datasets, identify trends, and predict outcomes with unprecedented accuracy. 

The adoption of AI in clinical research is not without its challenges. Ethical considerations, data privacy concerns, and the need for robust validation processes are critical factors that must be addressed. The interpretability of AI models also remains a significant issue, particularly in highly regulated industries like healthcare. Additionally, many disparate AI systems are being offered within clinical trials, which creates challenges in terms of interoperability, integration, scalability, and global support.

Despite these challenges, the potential for AI and ML to revolutionize clinical research is undeniable. By harnessing the power of data and AI, researchers can unlock new insights, accelerate the pace of discovery, and ultimately improve patient care on a global scale.

Summary

The journey of AI from its inception to its integration into clinical research represents a paradigm shift in how we approach problem-solving and decision-making. By embracing AI and ML tools and systems, researchers can navigate the complexities of modern healthcare with confidence—paving the way for a future where data-driven insights drive innovation and progress.

Discover how Medidata is at the forefront of AI in clinical trials.