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
Technological Optimism: Khosla believes the current innovations in biology, research, and healthcare will lead to an explosive combinatorial effect, catalyzing rapid progress in the coming 20 years. He draws parallels to the magnitude of change seen between 1900 and 1920, which may be difficult for us to imagine fully. Khosla warns that although many possibilities exist, predicting the exact path of these changes is challenging due to uncertainties.
AI in Healthcare: Khosla emphasizes the potential of AI to multiply doctors’ capabilities and create better, more affordable healthcare accessible everywhere. He highlights the current reality of healthcare, where excellent care is limited to a few, and many in developing countries lack access to basic care.
Essential Steps for Improvement: Khosla stresses the need for dedicated efforts to realize these possibilities. He cites the example of electric cars, where experts’ predictions fell short due to underestimation of technological progress. He advocates for individuals like Elon Musk who drive change through sheer willpower and determination.
Examples of Implementation: Khosla shares an example of his son’s company, which aims to provide affordable 24/7 primary care via text messaging using AI. He emphasizes the gradual process of developing such systems, involving real doctors interacting with real patients while AI gradually takes on more responsibilities. He suggests that AI can be applied in narrow domains of psychiatry and cardiology, with the potential for broader applications in oncology and other fields.
Addressing Skepticism: Khosla acknowledges the skepticism surrounding AI’s role in healthcare, particularly among those who believe humans are better suited for medical care. He counters by pointing out that many people with cancer do not have access to oncologists or virtual tumor boards, which technology can make universally accessible. He advocates for multiple attempts at implementing AI in healthcare, learning from failures, and driving down costs while improving effectiveness.
00:09:10 Finding Passionate Experts to Drive Healthcare Innovation
Automated and Cost-Effective Care: High-quality healthcare demands automation for efficiency and cost reduction. Technology, not human labor, should drive cost-effectiveness in healthcare.
Human Element: Despite automation, the human element of care remains vital for patient well-being. Moving to a nursing facility despite better care can increase mortality rates due to emotional factors.
Expertise and Consistency: Expertise in healthcare should not vary significantly between providers. Care quality should be consistent regardless of affordability.
Passion and Expertise: Companies with passionate individuals driving the vision are more likely to succeed. Deep expertise is essential for developing innovative medical technologies.
Trial and Error: Many attempts fail before a successful medical technology is developed. Predicting success is challenging; high probability shots are necessary.
Luck and Innovation: Luck plays a role in scientific discoveries and innovations. Unexpected breakthroughs can improve existing technologies.
Optimism and Investment: The number of passionate experts and risk-taking investors fuels optimism. Investors should focus on potential success rather than avoiding failures.
00:12:57 Changing Data Landscape and AI in Healthcare
Sensor and Measurement Explosion: Sensors are becoming increasingly prevalent and efficient, leading to an explosion of measurement data. This shift from traditional lab tests to continuous monitoring allows for the collection of more comprehensive and informative data.
Predictive Biomarkers: Tom Insell, former head of psychiatry at NIH, highlights the predictive power of biomarkers collected from cell phones and devices. These biomarkers often outperform traditional psychiatric assessments.
Data Explosion in Healthcare: The combination of sensor data and AI/statistical engines is driving an exponential increase in data available per patient. This trend has been underestimated by healthcare institutions, resulting in data volumes that far exceed initial projections.
Example of Biomarker Measurement Company: A small company focused on biomarker measurements demonstrates the practical application of this data explosion. The company’s platform collects and analyzes vast amounts of data to provide personalized insights and predictions.
00:15:34 Data Generation and AI Transforming Medicine
Data Generation: Low-cost laser printer-like devices enable primary care offices to test patients for almost everything on a quest test. Companies aim to perform proteomics and metabolomics for under $100 per dried blood spot, leveraging the affordability of mail delivery.
Impact of AI on Medicine: DeepMind’s protein structure prediction using AI exceeded the best research worldwide in this field, demonstrating a quantitative jump in performance. 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.
Symptoms and Biomarkers: 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 and Network Science: 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.
Conclusion: Large-scale data generation and AI are revolutionizing medicine, leading to surprising results and more precise and effective treatments.
00:18:58 AI in Healthcare, Personal Computing, and the Future of Medicine
Data Privacy and Transparency: Transparency is crucial in data sharing agreements. Contracts should explicitly state data sharing terms and purposes, allowing users to make informed choices about their data. The problem with data hasn’t been sharing but rather the lack of transparency.
Telemedicine and Technology Integration: Telemedicine today involves basic doctor-patient interactions over phone or video calls. 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.
Enhancement of the Human Form: 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.
Future of Healthcare in 5, 10, and 20 Years: 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.
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.
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