Based on what I have seen, many companies are still shy of using AI. The efficiency and ease of AI is a double edged sword. It can take away jobs, and there are still inquisitions about accuracy. A side question I have is: Does the FDA have any regulation or recommendation about how AI can be used in manufacturing or quality processes? Besides that, I believe there is a personal component that is involved. Engineers are generally interested in innovation and technology. However, some executives and engineers can be uncomfortable with the idea of AI being used. They can have legitimate concerns, or it can be personal beliefs that might be getting in the way. Also, the use of AI depends on the company. A larger (richer) company have the capability to implement AI on a wide scale across many disciplines in the company, from documentation or quality control on the manufacturing floor. Smaller companies may not have that ability, due to costs. I echo the other posts in this thread where engineers must be up to date as to what types of AI are being used and the innovations in it that are associated. It would be unfortunate and foolish for a company to have the ability to implement AI, but did not due to lack of knowledge or attachment to "the old ways". 

This was a great discussion to mention. Since we are now entering the age in which the world is beginning to heavily rely on artificial intelligence its is almost certain that it will be in every industry. For example, in quality assurance let's say there is a certain process which they need to monitor for a certain device. If there is a model created to track for certain indicators it can prove to be quicker and potentially more accurate then a human. It may also find ways to make the process more efficient. To answer how can you stay up to date is fairly simple. Ensure that your company has a team that not only watches but invests in emerging technologies as it could prove to be useful 

I believe that emerging technologies would be very valuable in the field of QA. With the use of AI, quality management systems could improve by more easily detecting problems with the systems to prevent audits from finding. This way, the system can improve much quicker be much better prepared for audits. Also, when doing quality control, the AI can be used to better find the root cause of a non-conformance, and much quicker find a solution.

I think that with technology that is arising as advancements are made it is necessary to keep up with them and try to find a way to integrate that into all parts of the world. QA/ QC would definitely benefit from it as they will be able to more easily catch the errors in parts that could be missed from the human eye. It is technology that should be taken advantage of because it makes the work a lot easier, but then the question comes up will AI take over the jobs of quality personnel. I personally think that it will be a while till AI does full replacement because the AI will need to be trained about what to accept and what to avoid. It could replace the amount of people that are necessary and could easily lower the costs for companies when it comes to hiring QA/QC. It is quite a beneficial advancement that should be used more because it will only make life easier. The thought process of planning what steps need to be taken will need to be made from people, but overtime if the AI is taught to plan certain steps using older plans it can take that as inspiration and actually find the most efficient method. 

Emerging technologies like IoT, AI, and machine learning are transforming how work is done at a rapid pace and is arguably making quality assurance for medical devices more safe. I feel the best way to keep up with the large influx of automated technologies is to tune into scientific research newsletters, research publications, or even through social media. In terms of further improving the implemented AI-driven inspection tools is through machine learning, which can be used to predict problem areas and improve test coverage. Additionally, through trial and error in practice runs can further improve the machine learning of the AI and yield similar if not better results than current human visual inspection and it might potentially further streamline the inspection process. 

I think technology is always evolving and it is doing so rapidly so I do believe that QA should adapt to the rapidly changing technology. Tools like AI can be beneficial in making processes go faster and improve outcomes. I do believe that people working in QA should stay up to date with the changing technology and be trained on how to use it to make things more efficient in the workplace. I believe implementing new technology can help companies ensure QA systems become more efficient.

Specifically focusing on AI, it could greatly impact the jobs of QA individuals and aid them in making more effective SOPs, validation documents, etc. Utilizing AI to help improve certain processes and making better validation methods could help not only improve the device but save money and time. AI is able to catch certain things that humans cannot, and once it is refined and taught properly, AI can in a sense replace certain aspects of a QA job. Although this would be years down the line (not now of course), it could serve as an effective tool to not only find errors, but find optimal solutions to those errors, ultimately helping the processes become better and help the quality of the end product improve. With machine learning models, you can also make different models to see if any changes or improvements to certain processes could help improve their efficacy. There is almost unlimited potential with AI, because it does not necessarily fully replace humans, but in a sense, it gives another perspective on how to tackle problems that may occur or just ways to improve.

Emerging technologies like IoT, AI, and machine learning are transforming how Quality Assurance is carried out by introducing automation, predictive analytics, and realtime monitoring. One effective way to keep up is by integrating these tools into the QA workflow rather than viewing them as separate systems. For example, AI-driven data analysis can detect subtle patterns of failure or deviation that human inspectors might miss, while IoT sensors allow continuous monitoring of production parameters to ensure consistency. Machine learning models can even predict potential quality issues before they occur (remain proactive). Of course, consistent research as well would be beneficial for staying up to date with current applications and data.

I think bringing emerging technologies into quality assurance could really help companies work smarter and more efficiently. Tools like artificial intelligence, the internet of things, and machine learning can make it easier to create quality procedures and prevent issues before they happen. This shift toward a more preventative approach could save time and resources while improving overall accuracy. One part that really stood out to me from the lecture is traceability. Being able to trace every step of a process, from materials to final inspection, can make a huge difference during audits or emergencies. With real-time data collection, companies can quickly locate problems and make faster, more informed decisions. To keep up with these technologies, I think companies should focus on continuous learning and strategic adoption. Employees need training to understand how these tools work and when to apply them effectively. Technology should support human expertise, not replace it. What do you think companies can do to make sure automation improves quality assurance without losing the human judgment that keeps the process accurate and ethical?

You make a great point about AI being a double-edged sword; its potential for efficiency can clash with legitimate concerns about accuracy, bias, and even job security. I agree that implementation often depends on company size and available resources. Larger organizations tend to have the infrastructure and budget to validate AI tools, while smaller ones might hesitate due to cost and regulatory uncertainty.

The FDA has actually started to address AI in manufacturing and quality processes, especially under its Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan. Although this mainly applies to software used clinically, it sets a precedent for transparency, real time learning algorithms, and validation expectations that could extend to manufacturing QA systems as well. I believe adopting AI successfully in QA comes down to balancing innovation with accountability

Newer technologies throughout history have always disrupted some way of life. It is the nature of society to continuously adapt and overcome new technological advances whether thats by slightly changing the aim of the company or technology or to completely start down a new line of work. I think the only concern that is brought up by the advancement of technology is AI, but still that allows for many significantly stronger systems. There is and always has been a fine balance when it comes to the work place and i think using newer tech has its benefits but also flaws for QA. For example it may be better at detecting a product deformity than a human would be.