Vinod Khosla (Khosla Ventures Founder) – Wearable Tech + Digital Health + Neurotech conference at Stanford (May 2019)


Chapters

00:00:03 AI in Healthcare: The Promise of Better, More Accessible Care
00:09:10 Finding Passionate Experts to Drive Healthcare Innovation
00:12:57 Changing Data Landscape and AI in Healthcare
00:15:34 Data Generation and AI Transforming Medicine
00:18:58 AI in Healthcare, Personal Computing, and the Future of Medicine

Abstract

Revolutionizing Healthcare: The Convergence of AI, Data, and Innovation

The Dawn of a New Era in Medicine

Vinod Khosla, a visionary in technology and innovation, predicts a transformative future for healthcare, fueled by groundbreaking advancements in biology, research, and particularly, artificial intelligence (AI). This article delves into Khosla’s perspectives, exploring how AI and data are poised to revolutionize healthcare delivery, accessibility, and effectiveness.

Technological Optimism and Predicting the Unpredictable

Khosla, a self-proclaimed technology optimist, anticipates a future where the combined effect of innovations in various fields leads to explosive progress, surpassing the collective achievements of the past century. He acknowledges the unpredictable nature of technological advancement, citing Tesla’s unexpected rise and the underestimation of electric car adoption as examples of the inherent uncertainties in predicting innovation paths.

AI as a Healthcare Game-Changer

Khosla envisions AI dramatically enhancing healthcare, paralleling the impact seen in the evolution of electric vehicles. He foresees AI not only augmenting the effectiveness of medical professionals but also making healthcare more accessible and affordable. The potential of AI is further illustrated through examples like 24/7 primary care via text messaging, reminiscent of the convenience and affordability of a Netflix subscription, and its applications in psychiatry and cardiology.

Overcoming Skepticism and Embracing Challenges

Despite skepticism regarding AI’s role in healthcare, particularly around the irreplaceability of human expertise, Khosla emphasizes the pressing need for technological solutions in underserved communities lacking quality healthcare. He stresses the importance of multiple attempts and iterations in AI development, drawing parallels to Facebook’s journey in social media.

Key Points: Balancing Automation and the Human Element

In this transformative journey, balancing automation with the human element is crucial. High-quality healthcare requires automation for affordability, yet the human aspect, such as the placebo effect, remains vital. Healthcare quality should be consistent, not fluctuating based on socioeconomic factors. The drive for healthcare innovation demands entrepreneurs with deep passion and expertise. Furthermore, the sector requires investors ready to support high-risk, potentially revolutionary ventures.

The Data Explosion and Its Implications

Khosla discusses the proliferation of sensors and measurement technologies, leading to an exponential increase in healthcare data. This surge, exemplified by a significant underestimation of data growth per patient, highlights the need for scalable data management solutions. The practical applications of this data explosion are evident in biomarker measurements and the affordability of comprehensive testing.

AI’s Role in Advancing Medical Science

DeepMind’s protein structure prediction using AI exceeded the best research worldwide in this field, demonstrating a quantitative jump in performance. Low-cost laser printer-like devices in primary care offices enable comprehensive patient testing. Companies aim to perform proteomics and metabolomics for under $100 per dried blood spot, leveraging mail delivery’s affordability. AI and data generation feed on each other, leading to stunning nonlinear developments. In 20 years, most of today’s medicine will be obsolete due to these advancements. The notion of using symptoms to diagnose diseases will become obsolete as biomarkers provide more precise information. For example, SSRI responsiveness can be predicted using biomarkers, leading to more targeted and effective treatments. Humira, a $14 billion drug, may waste $7 to $10 billion due to patients not responding. Cypher in Boston uses network science to predict which patients will respond to Humira with high sensitivity and specificity, potentially saving significant costs.

Ethical and Societal Implications

Khosla underscores the importance of transparency in data sharing and the ethical considerations of human enhancement technologies. He advocates for open discussions about the societal impact and acceptability of such advancements. Transparency is crucial in data sharing agreements, with contracts needing to explicitly state terms and purposes. The problem with data hasn’t been sharing but rather the lack of transparency. Telemedicine today involves basic doctor-patient interactions over phone or video calls, but the integration of technology can enhance telemedicine by reducing costs and improving data capture for diagnosis. AI-driven telemedicine can offer more personalized and cost-effective healthcare services, especially in underserved areas. Technological advancements may lead to enhancements of the human form, raising ethical and social questions. Different societies may make different choices regarding human enhancement due to cultural, political, and economic factors. The consequences of these choices may impact global competitiveness and progress.

Envisioning the Future of Healthcare

In the future, symptom-based medicine is likely to disappear, replaced by predictive diagnostics based on genomic, transcriptomic, proteomic, and metabolomic data. Network medicine will play a crucial role in understanding the body’s complex systems and predicting diseases well in advance. Heart rate variability (HRV) and microbiome transcriptome analysis may provide early indicators of diseases like the flu and cardiac issues. The abundance of data will lead to numerous research opportunities, especially in determining causality from correlations.

In conclusion, Khosla’s vision extends to a future where diseases like heart disease, diabetes, and Alzheimer’s are predicted and prevented long before their manifestation, thanks to genomics and other advanced technologies. This optimistic outlook emphasizes the pivotal role of data in discovering correlations and causal relationships, paving the way for a healthcare revolution fueled by science and dedicated individuals.

Khosla’s insights offer a compelling glimpse into a future where healthcare is more effective, accessible, and personalized, driven by the relentless march of technology and data. As we stand on the cusp of these changes, the need for informed discussion, ethical consideration, and passionate innovation has never been greater.


Notes by: Ain