The Bible of AI™

International scientific and technical publication on Artificial Intelligence | ISSN 2695-6411 | Officially founded in September, 2019

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https://estadosia.files.wordpress.com/2020/06/linea-editorial.png | 27/05/2020. The Bible of AI | Our Journal identifies as a positive ideological current, logical and adjusted to the needs of society, our Conclusión and the following documents inside each area:

Areas

AI in Medicine: Conclusion & documents

Conclusion

Motto 0.- Research and education are the best instruments to lead Artificial Intelligence to a good end for Society and Medicine… This, and other areas that we include in this scientific-technical publication, is our contribution.

Motto 1.- We claim the value and the relevant role of the data scientist (his seriousness in the choice, his rigor with the data and his research in the right set) in conjunction with Machine Learning Always. Mott 2.- A pre-trained AI model without the rigor of medical science, leads to more errors and disorientation of medicine than anything else, for the moment. Motto 3.- Machine Learning models in Medicine only tested in the laboratory should not suppose realities for society beyond the scope of research. Motto 4.- In the medium term (5-7 years) Artificial Intelligence, together with data scientists and adequate explanations and standards, will be an invaluable tool for medicine.

Motto 5.- “ML is an art, not a science“: We believe and follow this principle too.

Document 1.- “Deploying an AI system by considering a diverse set of perspectives in the design and development process is just one part of introducing new health technology that requires human interaction. It’s important to also study and incorporate real-life evaluations in the clinic, and engage meaningfully with clinicians and patients, before the technology is widely deployed. That’s how we can best inform improvements to the technology, and how it is integrated into care, to meet the needs of clinicians and patients”.

Healthcare AI systems that put people at the center. Beede, E. Apr 25, 2020, Google Health. [Avalaible online https://blog.google/technology/health/healthcare-ai-systems-put-people-center/amp/]

Document 2.- Research of our Director on Machine Learning in Medicine on EU portal (Robotics vs COVID19), COVID-19 detector using X-ray images. (Download here). And the annex as comment Using AI to fast and effectively diagnose COVID-19 in hospitals. (Download here).

Document 3.- The whole line of thought of these publications

https://estadosia.files.wordpress.com/2020/05/artificial-intelligence-and-machine-learning-in-software-as-a-medical-device_-discussion-paper-and-request-for-feedback-4.jpg

Artificial Intelligence and Machine Learning in Software as a Medical Device: discussion Paper and Request for Feedback

Artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. Medical device manufacturers are using these technologies to innovate their products to better assist health care providers and improve patient care. The FDA is considering a total product lifecycle-based regulatory framework for these technologies.

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The need for a system view to regulate artificial intelligence/machine learning-based software as medical device

FDA need to widen their scope from evaluating medical AI/ML-based products to assessing systems. This shift in perspective—from a product view to a system view—is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition

https://estadosia.files.wordpress.com/2020/05/a-human-centered-evaluation-of-a-deep-learning-system-deployed-in-clinics-for-the-detection-of-diabetic-retinopathy.jpg

A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy

This paper contributes the first human-centered observational study of a deep learning system deployed directly in clinical care with patients. Through field observations and interviews at eleven clinics across Thailand, we explored the expectations and realities that nurses encounter in bringing a deep learning model into their clinical practices. First, we outline typical eye-screening workflows and challenges that nurses experience when screening hundreds of patients. Then, we explore the expectations nurses have for an AI-assisted eye screening process. Next, we present a human-centered, observational study of the deep learning system used in clinical care, examining nurses’ experiences with the system, and the socio-environmental factors that impacted system performance. Finally, we conclude with a discussion around applications of HCI methods to the evaluation of deep learning algorithms in clinical environments.

Document 4.The essence of this tweet regarding importance of AI inputs / outputs data, and the validation of the results.


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