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a16z Podcast | Putting AI in Medicine, in Practice

a16z Podcast | Putting AI in Medicine, in Practice

22 segments available

There’s been a lot of talk about technology -- and AI, deep learning, and machine learning specifically -- finally reaching the healthcare sector. But AI in medicine isn’t actually new; it’s actually been there since the 1960s. And yet we didn’t see it effect a true change, or even become a real part our doctor’s offices -- let alone routine healthcare services. So: what's different now? And what does AI in medicine look like, practically speaking, whether it's ensuring the best data, versioning software for healthcare, or other aspects? In this episode of the a16z Podcast, Brandon Ballinger, CEO of Cardiogram; Mintu Turakhia, cardiologist at Stanford and Director of the Center for Digital Health; and general partner and head of a16z bio fund Vijay Pande in conversation with Hanne Tidnam discuss where will we start to see AI in healthcare first -- diagnosis, treatment, or system management -- to what it will take for it to succeed. Will we perhaps see a "levels" of AI framework for doctors as we have for autonomous cars?

Segments Timeline

1
0:00 - 0:46
0:46 duration159 words

Introduction to AI in Medicine

In this segment, Hanne Tidnam introduces the a16z podcast episode focused on the practical applications of AI in medicine. The discussion highlights the historical context of AI in healthcare, dating back to the 1960s, and sets the stage for exploring how AI can be effectively integrated into medical practices today.

"hi and welcome to the a 16z podcast I'm Hannah and today we're talking about AI in medicine but we want to talk about it in a really practical way what it means to use it in practice and in a medical ..."

2
0:46 - 1:50
1:03 duration192 words

Historical Context of AI in Healthcare

The guests discuss the evolution of AI in medicine, tracing its roots back to the 1960s with early automated systems and expert systems. They explain how these systems were designed to analyze patient inputs and provide diagnoses, often outperforming average physicians, yet they have not been widely adopted in hospitals today.

"maybe just do a quick breakdown of what we're actually talking about when we talk about introducing AI to medicine what does that actually mean how will we actually start to see AI intervene in medici..."

3
1:50 - 3:00
1:10 duration209 words

Challenges of AI Deployment in Medicine

This segment delves into the challenges of deploying AI in healthcare, emphasizing that technical accuracy is not the only hurdle. The discussion covers financial models, reimbursement issues, and the misalignment of incentives within the healthcare system that complicate the integration of AI technologies.

"so basically doing what hypochondriacs do Yeah right so so Google isn't as in some ways an AI expression of that where it's actually used ongoing inputs in classification to do that over time much mor..."

4
3:00 - 4:10
1:10 duration216 words

AI's Role in Diagnosis and Treatment

The conversation shifts to the potential roles of AI in healthcare, including substituting and complementing doctors' tasks. The guests explore specific examples, such as radiology and wearable technology, highlighting how AI can enhance diagnostic capabilities and patient monitoring.

"you think about kind of a hospital from the CFO's perspective misdiagnosis actually earns them more money because when you miss diagnose you do follow-up tests right and those and our billing system i..."

5
4:10 - 5:30
1:20 duration277 words

The Complexity of Predictive Healthcare

In this segment, the speakers discuss the unpredictability of healthcare and the challenges of making accurate predictions about patient outcomes. They emphasize the importance of high-fidelity data from wearables and sensors, and how the absence of data can indicate health issues.

"of physicians going crazy and where it's going to be very very hard and that I think is the challenge in terms of both building developing these technologies commercializing them and seeing how they s..."

6
5:30 - 6:50
1:20 duration234 words

AI in Imaging and Diagnostics

The guests highlight the effectiveness of AI in imaging diagnostics, such as x-rays and EKGs. They explain how AI can provide provisional diagnoses without needing extensive context, making it a valuable tool in identifying conditions like arrhythmias and cancer.

"there's some inherent challenges in the nature of the beast yeah healthcare is unpredictable it's stochastic you can predict a cumulative probability like a probability of getting condition X or diagn..."

7
6:50 - 8:10
1:20 duration256 words

Reimagining Healthcare with AI

This segment explores the potential for AI to reinvent the healthcare system by enabling continuous monitoring and improving access to diagnostics. The discussion highlights the gaps in healthcare access and the importance of actionable insights for better patient outcomes.

"think that's where we've seen AI really pick up in imaging like studies it's a closed loop diagnosis you know there is a nodule on an x-ray that is you know cancer based on a biopsy proven later in th..."

8
8:10 - 9:30
1:20 duration282 words

Addressing Healthcare Gaps with AI

The conversation concludes with a focus on the significant gaps in healthcare access, particularly for chronic conditions. The guests discuss how AI can help identify undiagnosed conditions and improve patient outcomes while potentially lowering costs for healthcare payers.

"reinvent the entire medical system with this assumption that we have a lot of data intelligence is artificial and therefore they're for cheap so we can do continuous monitoring so one of the things I ..."

9
9:02 - 10:04
1:02 duration204 words

Data Collection vs. AI Necessity

This segment raises the question of whether AI is essential for early detection and treatment in healthcare. The discussion focuses on the importance of data collection and the potential for AI to enhance the accessibility of diagnostic tools, rather than solely relying on AI for analysis.

"which seems astonishingly low but that's that's kind of the fact there's kind of a gap right about a third of people with diabetes don't realize they have it about a fifth of people with hypertension ..."

10
10:04 - 11:10
1:06 duration213 words

Understanding AI's Role in Error Recapitulation

The conversation delves into how AI can replicate human errors in medical diagnostics, particularly in EKG readings. The speakers discuss the implications of AI making similar mistakes as humans, suggesting that this could provide a level of trust in AI systems while allowing for safe scaling in medical applications.

"a last mile ai problem so that if you want to scale the ability so to get this stuff okay so let's say we get to a point where our bathroom tiles have built-in EKG sensors and scales and the data just..."

11
11:10 - 12:43
1:32 duration275 words

The Future of AI in Medicine

This segment discusses the potential for AI to assist in medical diagnostics and treatment, comparing it to the levels of autonomy seen in self-driving cars. The speakers emphasize the societal implications of AI in healthcare, including the need for clear definitions of responsibility and risk management.

"the convolutional neural networks and to actually break that down for a second so are those confusion matrix so the confusion matrix is a way to graph the errors and which directions they go and so fo..."

12
12:43 - 14:59
2:16 duration468 words

Proactive Healthcare with Wearables

The discussion shifts to the role of wearables in healthcare, highlighting their potential to generate vast amounts of data. The speakers envision a future where AI can proactively alert patients to seek medical attention, transforming the traditional reactive model of healthcare.

"technical hurdle at this point right well and you can imagine just as for less a self-driving cars you have different levels of autonomy it's not nothing versus everything you know in convention level..."

13
14:59 - 17:10
2:10 duration367 words

Challenges of Data Quality in AI

In this segment, the speakers address the challenges of ensuring high-quality data for AI models in healthcare. They discuss the risks of overfitting and the importance of calibration in AI systems, emphasizing the need for diverse and representative data to improve the accuracy and reliability of AI in medical applications.

"computer it is very natural but I think we need a couple things to get there we need really dense high-quality data to train and the more data you put in a model I mean so much gene learning by defini..."

14
17:10 - 18:01
0:50 duration165 words

The Challenge of Limited Labeled Data

The speakers address the challenge of training deep learning models with limited labeled data in healthcare. They discuss the ethical implications of requiring large datasets for training and the necessity of high-quality labeling to avoid overfitting. The conversation emphasizes the need for careful model design that aligns with the available data.

"a nice thing about wearable data is it fitbit's are the same all over the world this legal problem though is is interesting because you know in our context each label represents a human life at risk r..."

15
18:01 - 19:06
1:05 duration218 words

Engagement in Digital Health Studies

In this segment, the discussion shifts to the importance of user engagement in digital health studies. The speakers note that initial versions of health apps often fail to retain users, leading to high dropout rates. They argue that incorporating mobile design principles is crucial for maintaining participant interest and ensuring the success of health studies.

"and so don't complicate your models unnecessarily and don't build models that are overly complicated for the amount of data you have right because if you have the case where you're doing so much bette..."

16
19:06 - 20:01
0:54 duration187 words

Gamification and Motivation in Health Apps

The conversation explores the role of gamification in health apps as a means to enhance user motivation. The speakers discuss both extrinsic and intrinsic motivators, emphasizing the importance of providing users with insights into their health to encourage ongoing engagement with digital health tools.

"fundamentally a data quality problem if you collect data from all over the world you can address this but but you have to be a while for that to happen as we start gathering the data in different ways..."

17
20:01 - 21:22
1:21 duration282 words

Incentivizing AI Adoption in Healthcare

This segment focuses on the incentives for healthcare entities to adopt AI technologies. The speakers discuss the balance between risk and reward, highlighting the need for healthcare organizations to be willing to take on risks to realize the benefits of AI in terms of cost and improved patient outcomes.

"the initial version of versions of the apps weren't engaging so this isn't in ads an interesting new dimension as a medical researcher you might not think about building an engaging well-designed app ..."

18
21:22 - 22:43
1:20 duration274 words

Regulatory Challenges in AI Implementation

The discussion addresses the regulatory challenges associated with implementing AI in healthcare. The speakers highlight the evolving landscape of digital health regulations and the need for hospitals to navigate these challenges while ensuring patient safety and effective outcomes.

"entity whoever that may be whether its employer based programs insured based programs accountable care organizations are they going to be willing to take on risk to see the rewards of cost and scale a..."

19
22:43 - 24:09
1:25 duration305 words

Versioning AI Models in Healthcare

In this segment, the speakers discuss the complexities of versioning AI models in healthcare settings. They explore the balance between allowing models to learn continuously and the risks associated with incorporating bad data. The conversation emphasizes the importance of rigorous validation and testing to ensure that AI systems do not cause harm.

"version you could version every day every week every month it's browner's what you want to do is to the point that we're talking about earlier you want to bring in new validation sets things that it h..."

20
24:03 - 25:36
1:32 duration295 words

AI's Role in Clinical Settings

The speakers discuss the practical applications of AI in clinical settings, focusing on how AI can optimize scheduling and improve operational efficiency in hospitals. They highlight the primitive nature of current scheduling systems and the potential for AI to streamline processes, reduce human negotiation, and enhance patient care by predicting physician availability and patient needs.

"early companies used expert systems to just ease the pain points of me having to write out and code out every single diagnosis the super low-hanging fruit yeah can you improve the accuracy of physicia..."

21
25:36 - 26:59
1:22 duration279 words

Streamlining Healthcare with Startups

In this segment, the conversation shifts to the role of startups in healthcare, particularly those acting as full-stack providers. The speakers discuss how these companies can simplify decision-making for patients with chronic conditions by integrating AI into their services. They emphasize the potential for quicker adoption of AI technologies when startups work directly with self-insured employers.

"clinical setting you know the tools that we might see our doctor actually use with us or not I think it's going to be these adjacencies around treatment with management there are a lot of things that ..."

22
26:59 - 29:37
2:38 duration493 words

The Future of AI in Healthcare

The final segment examines the future landscape of healthcare with AI integration. The speakers speculate on how AI could reshape the industry by creating verticals focused on specific areas like diagnostics and therapeutics. They discuss the implications of AI on provider structures and the potential for enhanced accuracy in medical data, ultimately leading to a more efficient healthcare system.

"the AI inputs over time could be like well you can really truly know which physicians are quick and speedy which ones over there are a lot of times which patient cases might be high-risk which ones ma..."