HotpotBio is the open research group of Hotpot.ai, established to give back via biomedicine. Due to restrictions on publishing GenAI research, we launched this group to contribute to science in other ways.
HotpotBio is inspired by open source where ephemeral groups can drive innovation by attracting talent across organizational boundaries.
Ideally, the world magically cures cancer and creates AI doctors tomorrow, allowing this group to dissolve.
HotpotBio only exists because there are too many open questions.
Cancer is the second leading cause of death worldwide, claiming the lives of roughly 10 million people per year and devastating the lives of millions more [1].
With 8 billion people and only 12.7 million doctors, personalized healthcare is impossible today. Human doctors alone cannot bridge this gap and provide the attentive care everyone deserves.
The mission for HotpotBio is to advance cancer research and AI doctors with open research [2].
While curing cancer and AI doctors are fantasies today, many groups are working feverishly to close the gap between dream and reality.
One day, everyone will enjoy personalized healthcare supervised by Stanford-caliber AI doctors.
We hope to play a tiny part.
Even if the dream never materializes, it is better to aim for the stars and land on the mountains than to not aim at all.
The research is broadly organized into the categories below. Descriptions are tailored for non-technical audiences.
Our research investigates the association between Epstein-Barr virus (EBV) and cancer, concentrating on the topics below.
Viruses cause cervical cancer, Burkitt lymphoma, nasopharyngeal cancer (NPC), and several other cancer types, but the data is inconclusive for more common cancer types like breast cancer and lung cancer [3-8].
We welcome contributors of all backgrounds — healthcare professionals, academic researchers, software developers, ML engineers, or anyone passionate about healthcare and AI.
For aspiring founders, we hope HotpotBio offers a hub to connect medical professionals and technical individuals since startups are great vehicles for delivering change.
We welcome contributors in the areas below or any area of clinical relevance.
Work can fit any schedule and take one of many forms:
HotpotBio focuses on science, deferring policy and ethics to other forums.
Although this position may not appeal to all, the benefit of clear values is cultivating an environment where everyone can concentrate on science. Organizational theory demonstrates that teams united by shared priorities and explicit expectations tend to foster more productive collaborations.
I understand the anxiety around AI, but our culture is rooted in a deep study of technology history and societal progress. Throughout time, a consistent pattern has characterized the emergence of disruptive technology. This cycle was observed with books, computers, the web, and it's repeating again with AI. Fear dominates the discourse while concerned critics seek to curb capabilities and protect the masses.
With hindsight, we know those noble intentions were misguided and failed to account for the transformative benefits spawned by innovation. General technology, by definition, is wieldable for good or bad, but the good vastly outweighs the bad. This propels the world to greater heights of prosperity and accessibility.
On ethics, most people aspire to be moral and responsible, but the challenge is: whose values dictate tradeoffs and resolve disputes? Officials from California, Texas, China, India, France, Japan, the UK, Saudi Arabia, or where? Whose risk profile shines the way forward? For instance, GPT-2, GPT-3, and GPT-4 were all considered too dangerous for the average person, but those worries proved exaggerated at best and unfounded at worst. Moreover, it's presumptuous to assume one jurisdiction can bottle up software ingenuity or constrain global innovation. If America surrenders AI leadership, other nations will readily fill the void.
While healthy people can afford the luxury of endless deliberation, the sick cannot. With nearly 800K people passing away each month from cancer, discovering breakthroughs even one month sooner can save lives and spare immeasurable suffering.
Intelligent people may disagree. I respect different opinions and hope others can as well.
Novel datasets for TNBC and non-smoking lung cancer could power tens to hundreds of studies and hopefully set a new precedent for tackling tumor subtypes. See here and here for details.
We welcome partnerships with AI leaders to advance benchmarks and datasets for biomedicine. Long context, image, and video evaluations of frontier models are expensive. Credits and other support would accelerate these into reality.
Our GitHub page maintains the latest software, AI, and dataset developments.
Clarence Hu
