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Session 2 - January 10, 2023
Towards Robotics & Artificial Intelligence: Targeting Oral Health Care in Africa in the Digital Age
Obtaining, organizing, and sorting primitive data related to patients and students’ records is a daily process in dental schools. Whether handwritten or digitally inserted, this data contains valuable information that when properly processed, could reveal lots of information and clues in relation to oral health of the patients and performance of dental students. In the last decade, data mining became an attractive science based on core principles that allows the process of finding anomalies, patterns and correlations within large data sets to predict outcomes of interest. Unstructured data alone makes up 90% of the digital universe, but more information does not necessarily mean more knowledge. Data mining allows us to shift through all the chaotic and repetitive noise in our data, understand what is relevant and assess likely outcomes, and accelerate the pace of making informed decisions.
Learning Objectives
Obtaining, organizing, and sorting primitive data related to patients and students’ records is a daily process in dental schools. Whether handwritten or digitally inserted, this data contains valuable information that when properly processed, could reveal lots of information and clues in relation to oral health of the patients and performance of dental students. In the last decade, data mining became an attractive science based on core principles that allows the process of finding anomalies, patterns and correlations within large data sets to predict outcomes of interest. Unstructured data alone makes up 90% of the digital universe, but more information does not necessarily mean more knowledge. Data mining allows us to shift through all the chaotic and repetitive noise in our data, understand what is relevant and assess likely outcomes, and accelerate the pace of making informed decisions.
Learning Objectives
- Concepts related to statistics and data analysis.
- Principles of descriptive, predictive, and prescriptive data modeling.
| Time (EST) | Presentation |
|---|---|
| 1 p.m. |
From Regenerative Dentistry to Artificial Intelligence: Prospects for Improving Oral Healthcare
Rania El Backly, Alexandria Univ., Egypt Karim Fawzy, Cairo Univ., Egypt |
| 1:30 p.m. |
Mining Big Data: Research & Evidence-based Clinical Solutions
Moustafa Aboshelib, Alexandria Univ, Egypt Hala Ali Maher, Salman International Univ., Egypt |
| 1:50 p.m. |
Artificial Intelligence Powered Applications in Dentistry: Where We are Now and Future Directions
Moataz Khamis, Alexandria Univ., Egypt Rania Afifi, Alexandria Univ., Egypt Ahmed Mobarak, Alexandria Univ., Egypt Ahmed Holeil, Alexandria Univ., Egypt Hossam Mohamed, Alexandria Univ., Egypt Marwan Al, Alexandria Univ., Egypt |
Description
Session 2 - January 10, 2023
Towards Robotics & Artificial Intelligence: Targeting Oral Health Care in Africa in the Digital Age
Obtaining, organizing, and sorting primitive data related to patients and students’ records is a daily process in dental schools. Whether handwritten or digitally inserted, this data contains valuable information that when properly processed, could reveal lots of information and clues in relation to oral health of the patients and performance of dental students. In the last decade, data mining became an attractive science based on core principles that allows the process of finding anomalies, patterns and correlations within large data sets to predict outcomes of interest. Unstructured data alone makes up 90% of the digital universe, but more information does not necessarily mean more knowledge. Data mining allows us to shift through all the chaotic and repetitive noise in our data, understand what is relevant and assess likely outcomes, and accelerate the pace of making informed decisions.
Learning Objectives
Obtaining, organizing, and sorting primitive data related to patients and students’ records is a daily process in dental schools. Whether handwritten or digitally inserted, this data contains valuable information that when properly processed, could reveal lots of information and clues in relation to oral health of the patients and performance of dental students. In the last decade, data mining became an attractive science based on core principles that allows the process of finding anomalies, patterns and correlations within large data sets to predict outcomes of interest. Unstructured data alone makes up 90% of the digital universe, but more information does not necessarily mean more knowledge. Data mining allows us to shift through all the chaotic and repetitive noise in our data, understand what is relevant and assess likely outcomes, and accelerate the pace of making informed decisions.
Learning Objectives
- Concepts related to statistics and data analysis.
- Principles of descriptive, predictive, and prescriptive data modeling.
| Time (EST) | Presentation |
|---|---|
| 1 p.m. |
From Regenerative Dentistry to Artificial Intelligence: Prospects for Improving Oral Healthcare
Rania El Backly, Alexandria Univ., Egypt Karim Fawzy, Cairo Univ., Egypt |
| 1:30 p.m. |
Mining Big Data: Research & Evidence-based Clinical Solutions
Moustafa Aboshelib, Alexandria Univ, Egypt Hala Ali Maher, Salman International Univ., Egypt |
| 1:50 p.m. |
Artificial Intelligence Powered Applications in Dentistry: Where We are Now and Future Directions
Moataz Khamis, Alexandria Univ., Egypt Rania Afifi, Alexandria Univ., Egypt Ahmed Mobarak, Alexandria Univ., Egypt Ahmed Holeil, Alexandria Univ., Egypt Hossam Mohamed, Alexandria Univ., Egypt Marwan Al, Alexandria Univ., Egypt |