Bias in AI-Orthodontics

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00:00:17: Today, we're going to be talking about AI and orthodontics.

00:00:19: Dr.

00:00:20: Baxman has been talking about this a lot.

00:00:22: he shared some really interesting insights about how AI can change everything about orthodonics.

00:00:27: It's pretty amazing even from just the diagnosis-to treatment planning.

00:00:32: Yeah it's like having this super brain working alongside Orthodontists.

00:00:35: that is pretty wild!

00:00:36: It

00:00:36: really is.

00:00:37: And his right at front of all these.

00:00:40: But hes also been raising questions about AI especially when its comes to bias And that's really what we want to unpack today.

00:00:48: Like, What does bias in orthodontic AI even mean?

00:00:53: Where does it come from?

00:00:54: Why should you

00:00:54: care?".

00:00:55: It's really important because we're talking about things like misdiagnosis treatment plans That maybe aren't right for you and even bigger issues like fairness and equality In health care.

00:01:05: So let's break this down.

00:01:07: Let's say You go the orthodontist Right!

00:01:10: And AIs used as part of your Care for that AI to be biased.

00:01:16: And what kind of impact could have on you as the patient?

00:01:39: Image recognition soft is another example.

00:01:41: Well, effectively the device's looking at the image except what does it look?

00:01:46: That

00:01:47: kind of unsettling like we're talking about our health here.

00:01:49: We trust medical professionals to get things right.

00:01:52: yeah Yeah How does this bias sneak into AI?

00:01:55: well

00:01:55: a lot of it has to do with the data.

00:01:57: Dr.

00:01:57: Baxman as talked about how a lot Of these orthodontic datasets aren't as diverse as they need To be and there not his large.

00:02:05: And that's a big problem because AI learns from the data it is fed.

00:02:09: If this data isn't incomplete or doesn't represent the full range of patients and cases, then AI will pick up those limitations.

00:02:18: If an AI is mainly trained on data by people with certain ethnicity, it might not be as accurate when diagnosing conditions more common in other ethnicities?

00:02:29: Is that what we're getting at?

00:02:30: That exactly!

00:02:31: A classic example how dataset limitations create bias.

00:02:35: the AI just hasn't been exposed to enough variation, to be accurate across-the-board.

00:02:40: Okay and Dr.

00:02:41: Baxman he also highlighted another kind of bias I think selection bias right?

00:02:46: Yes

00:02:47: yes if i'm remembering that correctly.

00:02:49: Selection

00:02:49: bias is tricky.

00:02:50: it happens when the data used to train the AI has chosen in a way that researchers are developing an AI to diagnose specific jaw misalignment.

00:02:59: but imagine they only use data from patients who have already been diagnosed with this condition.

00:03:04: They're missing out on all the patients who might have it but haven't been diagnosed yet, maybe because they don't have access to this same level of care.

00:03:11: That's selection bias and that can really mess with AIs ability to accurately identify condition in future.

00:03:18: So its like the AI is learning from a biased sample?

00:03:20: Right?

00:03:21: And that bias gets baked into decision making.

00:03:24: Yeah

00:03:24: exactly It's bit like training a chef only one type ingredient.

00:03:31: But they're going to really struggle when the encounter something new, right?

00:03:35: And that's what Dr.

00:03:36: Baxman has been saying.

00:03:38: we need this collaboration between orthodontist and data scientists To make sure that the data used to train AI is diverse and representative.

00:03:48: That makes a lot of sense but This is starting to feel like a bit of black box to me.

00:03:52: Yeah We are putting a lot trust in these systems.

00:03:55: It hard understand how their making decisions.

00:03:58: You've hit on a really important point, like the issue of explainability.

00:04:02: Especially with something called deep learning models which can be incredibly... It's not always easy to trace how the AI arrived at a particular decision and if we don't know what it is making choices.

00:04:14: How can you make sure those choices are fair or unbiased?

00:04:18: Okay So We have talked about where bias comes from.

00:04:24: What are real stakes here?

00:04:25: What are the potential consequences of this bias in AI?

00:04:29: The consequences can be serious.

00:04:31: Imagine you go to the orthodontist and the

00:04:33: A.I.,

00:04:34: because it's inherent biases, misdiagnoses your condition.

00:04:38: That could lead to getting treatment that don't need or not get a treatment that do needs.

00:04:42: Because the A I miss something important.

00:04:45: Yeah thats scary thought.

00:04:46: It is like the A Is making these decisions that have real impact on health But those decisions might be based on flawed information or incomplete data.

00:04:55: And it goes beyond individual cases, this bias can contribute to bigger systemic issues like inequitable healthcare outcomes.

00:05:03: What if people from certain backgrounds consistently get worse care because the AI is making biased recommendations?

00:05:10: That's a major ethical concern.

00:05:12: Almost like biases that already exist in society are being reflected and amplified AI systems that we're using and that has the potential to really exacerbate existing inequalities in healthcare.

00:05:28: And then there's the issue of trust,

00:05:30: right?

00:05:30: If people start losing faith in AI because of these biases and errors it could slow down the adoption of this technology even if it has the Potential To Help People.

00:05:42: Bias leads to mistakes.

00:05:44: Mistakes lead to distrust And that could hinder progress in the field.

00:05:52: Absolutely, and let's not forget legal and ethical ramifications.

00:05:56: You know we can be looking at discrimination lawsuits violations of medical ethics.

00:06:01: It is a minefield nobody wants to navigate.

00:06:03: So how do you even begin address this?

00:06:06: Is there a way to spot these biases before they lead to real harm?

00:06:09: Are there any red flags that we should be looking out for.

00:06:11: One of the key things is to look at how the AI performs across different groups of patients, are their disparities in treatment recommendations or certain groups consistently getting better or worse outcomes?

00:06:23: These are signs something might off.

00:06:25: It sounds like vigilance is key but what about solutions?

00:06:29: What can actually do to mitigate this bias?

00:06:31: I know Dr.

00:06:32: Baxman has been a strong advocate for taking a proactive approach.

00:06:37: He has, and for good reason.

00:06:39: It's not enough to just acknowledge the problem.

00:06:41: We need to actively work To make AI in orthodontics fair And equitable For everyone!

00:06:53: all patients.

00:06:53: Exactly!

00:06:54: Where

00:06:54: do we start, what are the concrete steps that can take?

00:06:56: A lot of it comes back to data.

00:06:58: We need make sure AI is trained on datasets diverse in terms of age, gender ethnicity socioeconomic background and any other factors that could contribute to bias.

00:07:09: It's about building AI that is trained on a much broader and more inclusive set information.

00:07:14: right how do we actually go back doing it?

00:07:16: It's a multi-pronged effort.

00:07:18: We need more robust data collection practices ensuring the were gathering information from wide range patients cases is accurate and representative.

00:07:28: Are we talking about completely overhauling the way data is collected in orthodontics?

00:07:33: It sounds like a massive undertaking...

00:07:36: ...it IS A BIG CHALLENGE, but it's essential!

00:07:38: And thankfully there are brilliant minds working on this right now.

00:07:41: Researchers are developing new techniques to detect and correct bias during the AI development process.

00:07:47: Its' Like building checks & balances into system itself.

00:07:50: So

00:07:51: its not just gathering more data.

00:07:54: It's about being smarter, about how we use it and making sure that the AI is designed in a way that minimizes bias from the ground up.

00:08:02: And

00:08:02: transparency is another huge piece of the puzzle.

00:08:05: The more we understand how these AI systems work... ...the better equipped we are to identify an address any biases that might crop-up.

00:08:12: It's like shining a light on the process!

00:08:15: Making sure everything is open and accountable.

00:08:19: if its so difficult to completely eliminate bias What's the realistic goal here?

00:08:25: Is it even possible to achieve true fairness in AI mm-hmm?

00:08:29: That's a great question and one that experts are grappling with right now.

00:08:33: Dr.

00:08:33: Baxman himself has acknowledged that It's an ongoing challenge, it's not like flipping a switch And suddenly all AI is perfectly unbiased.

00:08:41: so its about continuous improvement rather than reaching some kind of flawless end state.

00:08:46: We need to be realistic About the limitations but we also can't let that stop us from striving for fairness in AI.

00:08:54: The stakes are simply too high to ignore the

00:08:56: problem.".

00:08:57: That's a powerful point, so where do we go from here?

00:09:00: What can our listeners do to be part of this conversation and help shape the future of AI and

00:09:06: orthodontics?".

00:09:06: Well...

00:09:07: For starters, awareness is key!

00:09:09: The more people understand potential for bias in AI…the more likely We need to keep talking about this, keep asking questions and keep pushing for progress.

00:09:19: So it's about using our voices and our choices to advocate for change.

00:09:23: but also sounds like we need to be savvy consumers of information especially when it comes to AI?

00:09:28: How can we spot potential bias in the AI systems that might encounter not just in orthodontics or other areas of life?

00:09:35: That is such an important point!

00:09:37: A healthy dose of skepticism always a good idea.

00:09:39: Don't be afraid to ask questions about how AI systems are being developed and tested.

00:09:44: Look for evidence of bias in the results they produce, and challenge the status quo!

00:09:49: Don't just blindly accept what that technology is telling

00:09:51: you.".

00:09:52: Yeah it's encouraging to see these conversations at least happening...that folks like Dr.

00:09:57: Baxman really pushing change.

00:10:00: but I think its important to remember that AI itself isn't inherently biased right?

00:10:06: That's a

00:10:06: very important point.

00:10:07: AI is a tool, and just like any tool it can be used for good or bad.

00:10:12: The bias doesn't come from the AI itself—it comes from us —the humans who create and use

00:10:18: it.".

00:10:19: So its all about how we develop AI...how we train it…how we apply it?

00:10:23: AI

00:10:24: algorithms are only as good at data that they're trained on ...and intentions of people building them.

00:10:31: Dr.

00:10:31: Baxman often talks about his ABCD system, which simplifies orthodontic care case planning.

00:10:38: It's a great example of how human expertise can be combined with AI to improve patient care.

00:10:45: AI within an orthodonic name and record of experience is evident in these images.

00:10:50: Yeah it's not about replacing human judgment.

00:10:52: its about augmenting.

00:10:53: I

00:10:55: love that, combining the best of both worlds.

00:10:57: The key is to approach AI with a sense of responsibility and make sure it's aligned with our ethical values.

00:11:03: We need ensure that AI is being developed in a way that benefits everyone not just select few.

00:11:11: That means building safeguards promoting transparency and constantly evaluating impact on these systems.

00:11:18: It sounds like transparency really crucial here too understand how AI makes decisions so that we can identify and correct any potential biases.

00:11:29: Transparency

00:11:29: is absolutely essential, Dr.

00:11:32: Baxman has also spoken about lean orthodontics which uses familiar analogies to make complex concepts easier to understand it's and more understandable so that we can use it more effectively and ethically.

00:11:46: That's a great point!

00:11:47: It is not just about the technology itself, but how to communicate with people about it.

00:11:52: We need to empower both patients' health care providers to really understand AI and its limitations.

00:11:58: Dr.

00:11:58: Baxman?

00:11:59: Precisely...we

00:11:59: have to be having open and honest conversations.

00:12:03: benefits, the risks of AI and if we can do that I'm optimistic that AI can truly revolutionize orthodontics.

00:12:11: Imagine a world where everyone has access to high-quill personalized orthodonic care regardless of their background or circumstances?

00:12:18: That's the future that i believe we can create with responsible AI development!

00:12:29: and feel empowered to be a part of this really important conversation.

00:12:36: Until next time, keep asking questions…and stay curious!

00:12:40: The future of healthcare is in our hands.