What can AI do to improve healthcare? Kingsley Ndoh, founder of Hurone AI, talks with Ben Lorica about how Hurone is making cancer care more effective for people who are underserved by the medical system. He discusses how AI can streamline the medical process, both helping doctors to treat patients more effectively and making clinical trials more diverse.
Points of Interest
- 0:36: What motivated you to apply AI to cancer care? What problems are you trying to solve?
- 1:39: We need environments for training AI models that are effective for all populations.
- 2:31: Current oncology solutions serve advanced healthcare systems, leaving community oncology centers and international markets underserved.
- 3:31: Lack of diversity in clinical trials means we don’t have full evidence on the efficacy of drugs.
- 5:00: What is an oncologist?
- 6:10: Cancer is a very complex disease; every cancer is different and has its own solutions.
- 6:43: What advantages do you bring as a domain expert?
- 7:11: I’ve been a physician taking care of patients. I understand clinical workflows in Nigeria and the US. I’ve also been an entrepreneur since I was in high school. I’ve also worked in the global oncology space with governments and pharma companies. That network is very important.
- 9:15: What was the situation before Gukiza [Hurone’s app]? What does Gukiza enable today?
- 9:44: Gukiza makes care more accessible to patients and optimizes workflows for oncologists. They may have to travel long distances to see an oncologist; they may have side effects or even emergencies that are avoidable; data about events may be lost.
- 12:53: Gukiza streamlines the process; it’s a two-way system that can be used standalone. There is a HIPPA-compliant API that can be integrated into major electronic medical records systems. Patients aren’t limited to an app; there is an API for WhatsApp, Telegram, and text messaging.
- 14:13: Patients can describe their problems. Clinicians can click a button and generate a response that they can review and send to the patient. Clinicians can also call patients, do clinical summaries, and see how patients are progressing.
- 17:08: One should think about this as a copilot. The app makes suggestions; the physician makes the decision.
- 17:35: There are definitely risks. We are building our model and fine-tuning it to ensure that hallucination is limited. But there is still a final human review.
- 18:40: What if I want to use the system in a completely new country? What does it take to get the system into a viable, usable state?
- 19:41: We conform to the country’s guidelines for the management of patients. Cancer care is usually based on established guidelines. In the US, we have NCCN guidelines. To make sure guidelines are responsive to different regions, the NCCN looked at evidence for research done in different countries to harmonize guidelines. That gave birth to the resource stratified guidelines for regions like Sub-Saharan Africa. We don’t need to customize a lot.
- 21:38: We are also building agreements for access to de-identified cancer data. As we scale, it will get better.
- 24:02: Health data is the most sensitive data in the world, but also the most abundant. Compared to other industries, healthcare is lagging behind. But many regions are looking for disruption and innovation and are willing to be flexible to work with us.
- 25:20: Our solution isn’t a magic bullet, but it will shift the needle.
- 26:12: We are excited about LLMs with text and images. But before LLMs, people were excited about computer vision. What models are you using?
- 27:10: We’re relying on LLMs and NLPs. There are established startups with computer vision for radiology and pathology; we are partnering with those companies. The major data we collect is genomic data. We are also incorporating wearable device data with things like geolocation, sleep patterns, heart rates, etc.
- 28:28: Social determinants of health data are also important: ZIP code, employment status, activities, food.
