The Journey of AI in Life Sciences Started in the 1940s

The field witnessed a surge of interest in the 1980s, driven by advancements in machine learning and neural networks. However, AI faced setbacks in the following "AI winter", a period of diminished funding and lost optimism due to unmet expectations. The turn of the century saw a resurgence with breakthroughs in data availability, computing power, and algorithmic innovations, propelling AI back into​ mainstream applications.​

The 2010s witnessed the rise of deep learning, particularly convolutional and recurrent neural networks, powering remarkable achievements in image recognition, natural language processing, and gaming. This era laid the foundation for transformative technologies such as virtual assistants, autonomous vehicles, and smart devices. Amidst these developments, OpenAI's GPT (Generative Pre-trained Transformer) series emerged as a groundbreaking approach to natural language processing.​

The release of ChatGPT, a product of the GPT-3.5 architecture, marked a milestone in AI evolution. Trained on massive datasets, it demonstrated unprecedented language understanding, generating human-like responses across a myriad of topics. 

ChatGPT's release showcased the potential and challenges of large-scale language models, sparking discussions about ethical considerations, biases, and the responsible deployment of AI in various domains. For the first time AI was introduced to the general public, and almost all companies discussed how to apply or even set up projects.​

The journey of Artificial Intelligence (AI) in the Life Sciences started long before ChatGPT

In parallel to its broader evolution, the journey of Artificial Intelligence (AI) in the life sciences has been a remarkable expedition, intertwining technological advancements with healthcare and biological research. In the early stages, AI applications in life sciences were modest, with computational models assisting in basic data analysis and​ genetic sequencing.​

The late 20th century witnessed the emergence of bioinformatics, where AI algorithms played a role in deciphering and analyzing the vast amount of biological data. As genomics and proteomics accelerated, AI applications in drug discovery and disease diagnosis began to take shape. Machine learning algorithms proved instrumental in identifying potential drug candidates, predicting biological interactions, and analyzing complex genetic patterns.​

The 21st century marked a significant turning point as AI in the life sciences transitioned from supportive roles to becoming a catalyst for groundbreaking discoveries. Deep learning techniques, particularly neural networks, revolutionized image analysis in medical imaging, enabling more accurate and early detection of diseases such as cancer. AI also found applications in personalized medicine; tailoring treatment plans based on an individual's genetic makeup.

In 2011, Watson AI Marked a Breakthrough

With IBM Watson AI prominently made a claim to change healthcare fundamentally. Andrew McAfee, Principal Research Scientist at MIT and famed book author on AI stated in an interview with Smart Planet’s David Rotman on May 25, 2011, titled "The Next Frontier in Medical Diagnosis? A Conversation with Andrew McAfee": If and when Dr. Watson gets as good at diagnosis as Watson is at Jeopardy! I want it as my primary care physician".​

Not much later, in 2013, Wired Magazine headlined: IBM's Watson is better at diagnosing cancer than human doctors. In the same year The Atlantic on its cover asked, “Is Your Doctor Becoming Obsolete?”.​

Although IBM Watson was not able to live up to high expectations, a lot of AI activity has taken place over the years leading up to the current hype created by the release of Chat GTP by OpenAI and the subsequent release of a tide of AI tools to the public. For instance, DeepMind was founded in 2014 to apply deep learning to healthcare and other fields. It is later acquired by Google. In 2017 Arterys receives FDA clearance for first deep learning medical imaging analytics software to help diagnose heart disease. Babylon Health launches AI-powered symptom checker and health advice chatbot app in UK and Rwanda, in 2018.​

In 2020, Mount Sinai hospital trials AI platform

Then in 2020 Mount Sinai hospital trials AI platform to predict patient declines and provide early intervention. In Israel a year later startup Aidoc got FDA approval for its AI to detect various critical findings in CT scans. And in 2022 Meta AI released Galactica, a large language model for science. Later it was retired due to biases and failures.​

In recent years, AI's impact on especially drug development has been profound, with algorithms capable of predicting drug responses, optimizing clinical trials, and accelerating the identification of potential therapies. The integration of AI with high-throughput biological assays has enabled researchers to sift through vast datasets and extract meaningful insights, facilitating a faster and more targeted approach to scientific inquiry.​

This journey resulted in a synergy between AI and life sciences that could revolutionize healthcare. As AI continues to advance, it holds the promise of transforming diagnosis, treatment, and prevention of diseases, ushering in an era where technology collaborates seamlessly with biology to enhance human health and well-being. The ongoing narrative in AI's role in the life sciences underscores the immense possibilities and ethical considerations as we navigate the frontier of personalized and​ data-driven healthcare.​

AI Technology is only one of the challenges in Life Sciences

The above path of AI in Healthcare and Life Sciences shows two things, first that this is a long path that has been going on for long before AI gained widespread attention in 2023. Secondly, having the technology is part of the challenge, getting it to work for Life Sciences is a bigger, taunting endeavor. Even the likes of IBM, Meta and Google have failed in well-founded promising attempts. Tenthpin operating on the intersection of technology and operations, guides our customers to apply the fitting technology for the relevant use cases in the right manner. ​
This makes the difference between the next big trend and actual game-changing technology.​

Tenthpin helps Life Science companies to distinguish between true solutions and fad. To determine actual opportunities and to balance investment and returns. We put AI into the context of the business need, use and existing IT landscape and applications. As such we help our clients optimize the use of AI against the lowest effort and cost.