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It typically takes ten years and $2.5 billion to bring a new drug to market in the United States. However, artificial intelligence (AI) has the potential to revolutionize this process by significantly reducing time and costs associated with developing life-saving therapies.
As the CEO of Dotmatics, a company that provides software to over 2 million scientists worldwide, I witness the excitement researchers have for AI and its potential in their field.
I share their enthusiasm. With almost two decades of experience in software and technology, I have been part of the journey towards the point we are currently at – where technological and scientific advancements enable data science to keep up with the massive amounts of data generated by the scientific community.
Dr. Raj Jena, an oncologist at Cambridge Universities Hospitals NHS Foundation Trust, spearheaded research using the AI system “OSAIRIS.” (Microsoft)
So, what does this mean for the development of life-saving and quality-of-life-improving therapeutics?
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Utilizing the power of AI and its capacity to analyze huge volumes of complex data, researchers can predict drug interactions, assess toxicity, and identify potential inhibitions. This allows for faster and more cost-effective discovery of successful compounds.
This is not merely theoretical. Biotech startups like Relay Therapeutics and Recursion Pharmaceuticals have reported successful clinical trials of drugs developed through AI-powered processes. These drugs have progressed from laboratory and animal studies to being administered to patients.
Witnessing the potential of AI being realized is exhilarating. However, we must also acknowledge the challenges it brings.
Throughout my career, I have experienced firsthand how technological advancements can transform various industries in expected and unexpected ways. For instance, when I worked in educational technology and built a platform for students to share study materials, measures had to be implemented to prevent plagiarism. Now, with innovations like ChatGPT, educators are questioning the very definition of plagiarism.
However, I believe that a blanket moratorium on AI development, as some politicians and tech executives suggest, is not the solution. Instead, open and actionable discussions about the challenges and necessary precautions are needed.
Each AI expert may prioritize different concerns. Reflecting on my experiences in other industries as well as leading Dotmatics, here are some questions I contemplate:
Quality and accuracy are crucial for scientists at every stage of drug development, including human trials. Given the well-documented issue of AI “hallucinations” observed in large language model systems, how can we ensure that the insights guiding real-world treatment development are reliable and accurate?
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The ethical considerations surrounding AI are vast. How can we harness the power of genetic data while safeguarding individuals against potential harm? For instance, if health insurers gain access to certain genetic signatures, should they be permitted to deny coverage or increase premiums without the patient’s informed consent?
When will it be appropriate, if ever, to remove humans from the decision-making process? While industries like transportation move towards full autonomy, the healthcare sector is rightfully cautious. Even companies developing AI for diagnostic purposes emphasize that their products assist physicians rather than replace them.
Personally, I find it hard to envision a healthcare system devoid of human involvement. If it were to happen, the industry would need to reimagine everything from patient communication to liability frameworks.
However, I also wonder what will occur when AI can handle tasks that were traditionally assigned to early-career scientists and technicians. Researchers are understandably eager to leave behind the burdensome aspects of data analysis and annotation, but education must adapt to keep pace.
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Curricula must shift focus from wet lab skills to critical thinking, producing scientists capable of making informed research and business decisions. Ultimately, it is the scientists, in collaboration with AI, who will shape the future.
Intellectual property is the lifeblood of the pharmaceutical industry. Consequently, as AI generates novel drug candidates, questions arise concerning IP ownership. These legal debates are ongoing, and it raises considerations regarding patent infringement and the future of the patent system. The answers to these questions will impact the incentives underpinning drug discovery.
Addressing these questions requires stakeholders with conflicting interests to find common ground while prioritizing the voices of scientists – the very individuals these systems aim to empower.
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In the short term, the focus of the technology community should be on ensuring our existing systems are AI-ready. While experts debate what AI will look like in the next ten years, our immediate goal is to establish a reliable foundation by ensuring that scientists currently utilizing AI have access to clean, organized, and unbiased data. The promise of AI is only as good as the information it learns from.
We stand on the threshold of a revolutionary change in how life-saving drugs are developed and utilized. By approaching this crossroad thoughtfully and strategically, working together to address concerns and implement necessary safeguards, the AI revolution, alongside the researchers and scientists utilizing it, will positively transform scientific discovery.
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