Bias in Orthodontic AI: Recognizing and Mitigating Disparities
Artificial intelligence has revolutionized the various dental specialties and orthodontics is not an exception. The utilization of AI changes the whole face of orthodontic diagnosis, treatment planning, and clinical outcomes. No doubt AI has made the diagnosis more accurate, efficient, and without human errors. It also reduces the men’s force and fastens the treatment outcome clinical procedure by reducing workload.
However, every innovation has some limitations, and Bias in orthodontics is one of the AI limitations. The bias can be systematic errors that can lead to deviations in results, data, and clinical outcomes. They can impact the clinical outcomes and treatment efficiency. Let’s try to unfold some facts about it.
Origin of Bias in AI:
Artificial intelligence works by analyzing algorithms and using machine learning. AI identifies the anatomical structures and dental conditions by evaluating the existing data set. One of the other roles of AI is the prediction of treatment outcomes. These are very crucial steps and most of the time Bias can arise because of database limitations.
Bias by Dataset Limitations:
Orthodontic and dental health care datasets are often relatively small and only include limited population data. This limited scale can pose challenges for training AI algorithms effectively, as smaller datasets may not capture the full picture of patient diversity and clinical variability. The results drawn by limited data have a chance to raise the bias and affect the patient’s clinical outcomes. Artificial intelligence algorithms can’t detect the size of data and analyze its diversity. It’s the one limitation that can bring bias in orthodontics.
Selection Bias in Orthodontic AI:
Selection is one of the crucial steps in data analysis. Bias can arise when the process of selecting data is not well organized and excludes or include certain individuals or cases.
Orthodontic data include various patient treatment outcomes performed in different settings. If the data is not systematically organized AI cannot be able to withdraw the required results.
The presence of selection bias in datasets can significantly impact the performance and reliability of AI models. Biased datasets may lead to biased outcomes or recommendations which can potentially exacerbate disparities in healthcare delivery across different population groups.
Explainability in Orthodontic AI.
Explainability concerns the challenge of understanding AI’s decisions, especially in deep learning models known for their opaque workings. Non-explainable AI systems may harbor biases, including selection bias, which poses risks. Explainability in artificial intelligence refers to the ability to understand and interpret how AI models make decisions. This is particularly challenging in deep learning models, which are often characterized by their complex, opaque inner workings. The lack of transparency in these systems can lead to a range of issues, including the presence of hidden biases.
Bias Consequences Orthodontic treatment:
1. Misdiagnosis and Inappropriate Treatment:
Bias in orthodontics can lead to incorrect diagnoses which is the major reason for inappropriate treatment plans. Patients from car groups may receive incorrect diagnoses or less effective treatment recommendations which ultimately reduces the efficacy of AI-based treatment modalities.
2. Inequitable Healthcare Outcomes:
The other important implication of Bias in AI systems can enhance the existing healthcare disparities. Certain demographic groups might receive lower-quality care due role of Bias in orthodontics.
3. Loss of Trust in AI Systems:
There are still controversial opinions regarding AI introduction in the medical field. Bias and lack of transparency in AI systems can erode the trust among both patients and healthcare providers. If stakeholders do not trust AI treatment planning, they might use it less likely which can undermine their potential benefits.
4. Legal and Ethical Consequences:
Bias in AI systems can lead to legal and ethical issues. It includes the potential discrimination claims and violations of ethical standards in healthcare.
Can operators recognize the potential Bias?
Disparities and biases can be identified by evaluating the performance of AI models across different demographic groups. The AI bias can be reduced by analyzing the feature importance and decision-making pathways of AI models. Disparities in treatment recommendations or diagnostic accuracy for different demographic groups can also be highlighted in algorithmic bias.
Regularly monitoring and comparing treatment outcomes for different groups can help identify disparities. Discrepancies in these outcomes can signal underlying biases in the AI system.
Mitigating Disparities in Orthodontic AI:
There are a few important steps that can be taken to reduce the bias and disparities created by orthodontic AI.
- The data set disparities can lead to bias So to create fair and unbiased AI models it is crucial to collect diverse and representative data. This involves including a wide range of demographic variables such as age, gender, ethnicity, and socioeconomic status.
- We can implement techniques that can detect and correct biases during the model development phase. This can include algorithmic adjustments re-evaluation of the training samples, and fairness constraints in model training.
- The other solution is to increase the transparency of AI models. It can help identify and correct biases. Transparent models allow for greater scrutiny and validation by healthcare providers.
Let’s conclude the whole discussion. It’s very difficult to eliminate the bias from any AI algorithms. Diagnosis is fundamental in orthodontics Dr. Baxmann’s ABCD-System® makes case planning in orthodontics simpler and easier to understand., LeanOrthodontics® uses a familiar analogy from daily life. However, by adapting certain mitigation strategies we can reduce the disparities and gain more benefits from the new technology.
Citation:
- Nordblom NF, Büttner M, Schwendicke F. Artificial Intelligence in Orthodontics: Critical Review. J Dent Res. 2024 Jun;103(6):577-584. doi: 10.1177/00220345241235606. Epub 2024 Apr 29. PMID: 38682436; PMCID: PMC11118788.
- Baxmann, M., & Gronau , K. (2024). Dr. Baxmann´s LEAN ORTHODONTICS® – The Ultimate Practice Book Series for excellent Orthodontics: Case Planning Volume 3. https://www.amazon.sg/Dr-Baxmann%C2%B4s-LEAN-ORTHODONTICS%C2%AE-Orthodontics/dp/3948361673