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Artificial Intelligence Webinar: questions & topics

These 23 questions were asked during the Q&A section of the AI for Healthcare webinar delivered in November 2020. 
Select the questions you would most like to see answered during the next AI webinar.
You may select up to eight questions. 
1.As a doctor working from an AI company in San Francisco, how do we collaborate with Australia?
2.Real-world data is often at risk of bias and missing data. Do you have any recommendations on how data scientists should best collaborate with physicians and clinical scientists?
3.Any recommendation on how my department could me more involved with AI in Nuclear Medicine?
4.What skills are needed to be involved in AI research?
5.How can data linkage across different elements of health care (hospital, primary care, medication dispensing, death data) be used here?
6.Even if we have clean data and reliable algorithms, we have implementation deficit disorder. How do we get these AI models into clinical practice?
7.Health care places such an emphasis on clinical coding. Is there any progress in AI enabling this process?
8.Risks and benefits of using AI in primary and tertiary health care. Capacity building of health professionals in using AI.
9.Are there some examples of non-image based AI applications being used in clinical medicine?
10.Can AI be used to improve quality of care? e.g. antimicrobial stewardship, given a lot of hospitals use electronic prescribing and there may be inappropriate selections or durations of antimicrobial treatments
11.What technique, in your opinion, would you recommend for text classification in health informatics, would you recommend gpt-2?
12.Can AI be used to improve the running of small private practices?
13.Interested in AI predictive disease models - Could you give some insights on current development?
14.What are the AI applications in endocrinology?
15.Where do you see AI in healthcare in 10 years in Australia?
16.What about patients that don’t know how to tell us what their symptoms are?
17.Bias. We hear a lot around AI bias and methods to mitigate this e.g. counterfactual fairness. This concern is brought up a lot regarding racial bias in AI algorithms, including medical algorithms. Are the current methods for mitigating bias, technically easy and robust?
18.Can AI be created to review our clinical decision-making with the aim of reduce cognitive bias?
19.You talked about big data. What do you think, the role of 'small data' in medical AI?
20.How long will it take for your network to learn with a given data sets?
21.How to use AI clinically to reduce errors in diagnosis, using a usual PC?
22.How do we guarantee privacy of data?
23.Won't AI threaten privacy and patient's rights?