Episode description
This show was recorded 23 March 2023 and can be viewed on our website here. To join us live for future shows and ask your own questions, please view and sign up for upcoming events in our Knowledge Center.
Artificial Intelligence (AI) has the potential to revolutionize healthcare and the medical technology industry, with the ability to assist in clinical decision-making, improve patient outcomes, accelerate speed to market and improve efficiency. In this RQM+ Live! show, leaders from RQM+ and Giotto.ai will explore the current and future applications of AI in MedTech, including the use of AI in medical imaging, diagnostics, personalized medicine, remote patient monitoring and medical writing. They will also share their insights on the challenges they've experienced so far in the field, and how AI can help solve some of the hardest problems facing the healthcare industry.
This session is designed to be educational and conversational, so please come prepared with questions! The goal of this session is to talk openly and generate ideas about how AI can best benefit you and your organization, now and in the future. We predict companies in the MedTech space who successfully harness AI/ML will have a significant advantage over those that do not.
Panelists:
- Alaric Jackson, Chief Digital and Technology Officer – RQM+
- Amie Smirthwaite, BEng, Ph.D., Senior Vice President, Intelligence & Innovation – RQM+
- Francesco Palma, VP of Product – Giotto.ai
- Wallyson Oliveira, VP of Machine Learning – Giotto.ai
- Celeste Maksim, Chief of Staff – RQM+ (moderator)
Questions with timestamps:
3:07 -- Framing the conversation with definitions... what do these terms mean and how do they differ from automation?
6:50 -- Could you provide examples of how this technology is being used more broadly in life sciences today?
8:40 -- What about from a more specific MedTech perspective?
9:20 -- What about from the industry perspective in general?
10:18 -- How does the transparency and explainability of AI effect its adoption? How can we trust AI?
13:43 -- In a healthcare setting, how are regulators responding to this increased adoption of AI / ML?
14:40 -- If AI is based on pattern recognition, is it possible that when a large amount of misinformation is fed into the algorithm that it causes the AI to make a poor decision? How do you work to prevent this?
16:41 -- What could manufacturers do to leverage AI / ML in their workflows? For example, where do they have large pools of data that could lend itself to using AI / ML to improve the workflows of internal operations or compliance itself?
19:15 -- Once we're thinking about the implementation stage, what are some of the challenges that a manufacturer will encounter along this journey?
23:10 -- What advice would you give a manufacturer in how to approach regulators with a new AI device? Also, do regulators have the necessary expertise from a review perspective?
26:57 -- Regarding data sets, if manufacturers are leveraging consultants or IT groups, how can they work to be sure the data set models are maintainable?
30:57 -- What are you thoughts about AI taking over roles?
36:05 -- How would you avoid bias when using ML and that data?
41:29 -- How would you recommend the listeners get started?