Artificial intelligence (AI) is growing at an explosive rate. Far from being a theoretical discipline, AI applications are embedded in our everyday lives – from navigation software to facial recognition, voice assistants, search engines, grammar and spell check tools, and even our personalized social media feeds. AI is having a similarly transformative impact on the pharma industry. But these are early days for generative AI pharma use cases, and the exponential growth anticipated by Moore’s Law, among others, makes speculating on even the industry’s short-term future nearly impossible.
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Let’s explore some of the current AI applications in life science and discuss what HCPs, data scientists, and futurists expect an AI-powered future might look like.
Types of artificial intelligence
There are three main types of artificial intelligence:
Artificial superintelligence (ASI), or super AI, refers to artificial intelligence that significantly exceeds human cognition in every conceivable way. This is the most speculative – and some would argue, far-fetched – interpretation of AI, which we’re currently somewhat distant from realizing.
Artificial general intelligence
Artificial general intelligence (AGI), or general AI, refers to AI that possesses cognitive powers equivalent to a human being. It’s sometimes called strong or broad AI because it can carry out many unsupervised tasks. However, like artificial superintelligence, AGI is still some way from becoming a reality. Even our most sophisticated and powerful AI tools are far from achieving the breadth and scope of human cognition, let alone consciousness – despite far outstripping human beings when performing certain, specific tasks. This brings us to narrow AI.
Artificial narrow intelligence
Narrow or weak AI is an artificial intelligence program designed to carry out a task with far greater alacrity than even the most highly-trained human. The everyday applications we mentioned earlier are all undertaken by narrow AI programs.
When we talk about the current AI applications in life science, we’re exclusively talking about programs built to solve specific problems that excel at what human beings are poor at, like analyzing large volumes of data, looking for patterns, recognizing anomalies, and automating processes.
The interesting paradox of AI is that it’s currently easy to create a computer program capable of carrying out the tasks most humans find difficult – like crunching complex equations, playing the stock market, or beating a chess grandmaster. In contrast, it’s almost impossible to create AI capable of doing what we do without thinking, like telling the difference between a car and a truck, reacting to physical stimuli in real-time, or extracting the meaning from a simple sentence. It’s the reason simple CAPTCHA security checks still work, and it’s the difference between narrow AI and general AI.
Machine learning (ML) is a specific type of artificial intelligence. Machine learning programs use data to improve their processes and learn – gradually becoming quicker, more powerful, and more efficient as they capture more accurate data. The voice recognition software in your cell phone or home assistant is an example of machine learning – building up a bank of training data from your speech patterns so it understands you better each time you use it.
What is generative AI?
Generative AI is a form of machine learning that can product text, video, images, and other types of content in the same medium in which it is prompted, such as text to text, or in a different medium from the prompt, such as text to image.
Applications for generative AI include creative and academic writing, translation, sound editing and image editing. Experts have raised concerns about generative AI, citing legal, ethical, political, social, and economic issues.
AI use cases in pharma
Narrow AI systems can parse and analyze enormous data sets with far greater speed, accuracy, and efficiency than even the most highly-trained humans, making them powerful tools for enhancing the drug development process. Here’s how pharma and ai can intersect.
Big data and life science
The term ‘big data’ describes data sets of such significant volume that they can’t be stored or processed by traditional data processing software. The field of big data exploded during the digital revolution of the early 20th century, and the life science sector produces more big data sets than almost any other. Everything from research data to electronic medical records (EMRs), clinical trial data, and the information captured by wearable technology is growing exponentially – providing both obstacles and opportunities for life science companies.
In a process known as hit identification, AI algorithms are used to search vast libraries of existing compounds to determine whether they could serve as active ingredients in new drugs. And in a similar process called precision medicine, AI programs churn through complex disease data sets, looking for patterns that might lead to potential treatment modalities. Genetic Engineering and Biotechnology News report, “The role of AI is to ‘connect the dots’ for enormous amounts of clinical, genomic, and patient data to find the potential utility of an existing drug for a certain condition or disease.”
AI systems are already widely used to help identify and generate novel drug candidates, repurpose existing drugs, and validate potential drug candidates. There’s also an opportunity for pharma AI and machine learning to crunch manufacturing process data and to make these processes more efficient – minimizing expense and reducing time to market.
Clinical trial optimization
The clinical trial process is long-winded, expensive, and often fails. But AI is already helping to make clinical trials more efficient, bring new treatments to market more quickly and maintain patient safety.
By parsing diverse EMR data, AI is improving trial recruitment by identifying populations with the best chance of responding to treatment. AI is being used to replace placebo control groups with ‘digital twins’ of subjects for randomized clinical trials, allowing clinical teams to reduce the size of their control groups and minimize patient disruption. AI applications are being used to clean, aggregate, store, and manage clinical big data – helping clinical teams inform better site selection, optimize study designs, and accelerate the informed consent process.
Predictive AI models can be used to calculate the results of a given treatment on a subject or group of subjects – potentially removing the need for animal testing and accelerating the preclinical phase. And finally, AI-powered wearable technologies are proving invaluable to clinicians as patient monitoring tools, enabling “automatic detection of physical and emotional states.” When asked what technologies would improve clinical trial efficiency most, “the top answer from survey responses was leveraging big data and AI.”
Diagnosis and disease identification
As mentioned, narrow AI applications are far more adept than humans at finding patterns, anomalies, and correlations in big data sets. This ability is beneficial for accurate diagnosis and disease identification. Soon, AI is expected to minimize human error in disease identification and make more accurate diagnoses faster and earlier than human HCPs – leading to significantly improved patient outcomes.
Most experts suggest that we shouldn’t view AI as something that’s going to replace doctors, but as a tool to support them – potentially creating a more equitable healthcare landscape capable of bringing quality care to more people around the world.
Diagnostic imaging represents one of the most promising avenues for AI in life science. A recent study revealed that narrow AI is already on par with human HCPs in analyzing medical images. Given that the rate of AI growth is expected to be exponential, this has significant implications for specialisms such as radiology, dermatology, pathology, and ophthalmology. AI is so efficient at differentiating between healthy and cancerous tissues that a school of thought suggests human radiologists and other imaging specialists won’t exist within a few decades.
However, AI still has a long way to go, and even when our narrow AI systems are far more advanced than they are now, human intervention may still be required. As Ohad Oren et al conclude: “Unless AI algorithms are trained to distinguish between benign abnormalities and clinically meaningful lesions, better imaging sensitivity might come at the cost of increased false positives.”
ChatGPT: right or wrong for pharma?
ChatGPT (GPT stands for “generative pre-trained transformer,” referring to how the chatbot processes requests and formulates responses) is an artificial intelligence chatbot that uses natural language processing to create conversational dialogue. It can respond to questions and compose written content like articles, social media posts, essays, and code. It’s available to the general public through OpenAI.
In healthcare-related settings, chatbots may be used to communicate with patients on behalf of healthcare service providers, to improve communication between doctors and patients, and to make healthcare more accessible. Applications for chatbots in these settings include mundane activities like scheduling appointments or ordering medical supplies to more complex operations like providing information about conditions or symptoms.
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ChatGPT use cases in pharma
In its publicly available form, ChatGPT isn’t the best fit for pharma, and organizations should be careful about using it. Inputting proprietary or sensitive information is unwise, and the chatbot may provide inaccurate, biased, or otherwise harmful responses.
However, the technology behind ChatGPT may have potential uses in specific settings, including:
- Drug information and assistance
- Patient support and engagement
- Adverse event reporting
- Medical query resolution
For the most part, teams should take rigorous steps to ensure they are not feeding proprietary or sensitive information into programs that may risk exposing that information.
For this reason, pharmaceutical teams need to create policies and standard operating procedures around the internal use of generative AI in pharma and work closely with technology vendors with experience in the pharmaceutical industry.
Within3’s assisted moderation for pharma
AI use cases in pharma include our own Moderator Assistant, part of Within3’s insights management platform. Moderator Assistant is built on proven AI capabilities and is unique to the industry, providing up to 7x the feedback of typical engagement settings like Zoom, Team, or in-person meetings.
This feature also eliminates about 90% of the work associated with moderating, analyzing, and reporting on insight-gathering activities like advisory boards, steering committees, and other common types of engagements.
Moderator Assistant is secure for pharma companies, unlike the consumer versions of publicly available chatbots like ChatGPT. Business teams can safely take advantage of the speed and convenience of generative AI while maintaining data privacy.
There’s a long road ahead for AI and ML in the pharma industry. While there’s no shortage of potential applications for AI for pharma and biotech, companies should proceed cautiously. Questions about security and data privacy abound, and pharma companies cannot risk running afoul of regulatory or compliance standards.
So why should pharma use AI?
Certainly, the amount of data pharma companies can access and use to inform their strategies makes AI applications very appealing. AI pharma innovation will allow companies to remain competitive in the evolving pharmaceutical industry. Patients will benefit from effective drugs developed quickly, with more reliable and accurate data sets. Simply put – AI enables the life science industry to innovate faster.
Learn more about AI and medical affairs in our podcast: AI is an opportunity for medical affairs.