Hi everyone, I'm Elham Nasarian, a PhD candidate in industrial and system engineer.
Hi.
Today I'm here to talk about an exciting research area, enhancing AI clinician interaction.
Building trust to improve patient outcomes. Today we are hearing many good news about the role of AI in healthcare. On the other hand, we are facing many challenges in this area as well.
Which recently, the World Health Organization calls for safe and ethical AI in medical setting.
So why they need that? It's because of the nature of AI tools. The AI tools could be categorized into different category five bugs machines, they have simple structure so the end user can understand it very well.
On the other hand, black box machine, they have more complicated structure. So the end users could not understand it very well and they could be confused.
And because of that, they may not trust the result. For this reason, recently researchers are trying to use explainable AI tools For example, visualization tool to only provide more understandable result for the output of black box machine So we are facing a big challenges also here because forgiving
Gaining the trust of clinicians of using ai result we need to provide more explainability in the whole process from the pre-processing data collection, post-processing model selection with their knowledge. So it's important to consider the whole process of AI implementation in the healthcare setting, not only
The output. For this reason we got motivated and interested to conduct a systematic review And in the identification part we collected more than 2,000 papers from PubMed with PubScience, and Scopus And we use Prisma method.
Through 2012 from 2020 23 and after screening parv included only 74 paper which includes 10 review papers and 64 research paper, you may wonder why did you also include that review papers because we wanted to provide comparison between our survey and current survey
And after that we found it out. Most of the current survey only focused on explainable AI post-processing in the healthcare application. Only a specific health application.
On the other hand. Our work has kind of limitation. It's about we only use open access peer reviewed papers And we didn't consider gray literature.
After analyzing these conducted research papers, we found out some comparison and provided this compassion. For example, in this part you can see most of the research paper only focus on the post-processing explainability and there is a big gap specifically in the data processing part.
On the other hand, as you can see, most of the research paper focused on the heart disease, mental health, diabetes.
And there is a big need, for example, in the rare disease. Also, due to the trend of using large language models and variable medical devices.
We provided this compassion as well and we can see there is also a big need to provide more transparent result for medical large language models or variable medical devices.
So after conducting all this analysis, we decided not only provide this analysis, but also provide a roadmap to implement this framework into real-world health application.
So the core idea of this roadmap or this framework, it's about collaboration, close collaboration between medical experts and AI expert in the whole process from the data preprocessing, model selection, and post-processing. And after that, it's very important to have a quality assessment in each step.
And then provide a specific or tailored output for end users based on their needs.
So at the end, I want to highlight that and what we need to do in the future and what we need to focus on that. It's about we need to stop thinking about explaining the output of ML or AI
Result. On the other hand we need to tailor the output for specific end users.
Also, we need to provide a trade-off between transparency and performance and develop more quality assessment metrics.
Thank you so much for giving me this opportunity. I really appreciate it.