The training of a model is influenced by: The amount and quality of Data it receives. The Processing Power: it is more expensive than you think and effective methods are ...
Overfitting Overfitting occurs when the model is accurate with the data it is supplied to predict and the model is quite accurate, so the training set and validation set agree. ...
Explainability is of upmost importance in health in general for explaining the benefit of the patient…and not the least gaining his or her trust. Explainability concerns the algorithmic solution and ...
Even if efforts are made to standardize the image collection (same position), there are distortions which can arise. These can sometimes be corrected but it is important to be aware ...
Taking a standardized clinical picture can help to compare images and enhance machine learning to recognize the conditions, especially when associated with clinical data. It is also of use when ...
The adage goes: garbage in garbage out, and this is especially true for machine learning models, since this data sets on which the models are trained and validated, are essential ...
In a recent paper (2020), clinical researchers from Stanford proposed the use of such a framework, to think about data and the development of AI; they were thinking about radiology ...
Many patients are supportive of the use of their data to improve healthcare and research, especially when it’s in the hands of trusted physicians, medical professionals and researchers….but they feel ...
Let’s apply 2 medical ethics principles on data and discuss some conclusions Beneficence One application of this principle to clinical data like dermatologic images, is that on an individual level ...
In terms of data, nowadays a fact that data is of great value. It can be traded and used as a currency to access entertainment or other services…if it’s free: ...
It is also called deidentification and as the term suggests consist of removing all identifiable features and in dermatology this would consist of: removing names and other upstream data and ...
There is hope of being able to achieve a wider coverage thanks to systems such as electronic medical records (EMR): There is a need, however, to overcome silos which constitute ...
With all that scattered data, here are 3 techniques used to analyze the data. Note that Artificial Intelligence techniques such as Machine learning automate the process which would otherwise be ...
This term sounds nice and it gives the impression of some satellite wireless communication breezing in the air ! However truth is more prosaic and down to the earth: the ...
To fulfill the Conditions for an operational distributed file system [see Storing data: Hadoop Distributed File System (DFS)], the CAP rule applies. “ C”, “A”, “P” stands for: consistency (C): ...
This storage system stands for Not only SQL (structured query language). SQL queries cannot be used to access those databases. The goal is to fulfill the requirements of big data: ...
Structured data For structured data, the volumes tend to be controllable and an efficient storing system through a relational database was classically enough until recently. The data meticulously organized in ...
Data is not everything and although it has been compared to oil, there are big differences Clive Humby, director of the Tesco Loyalty card program coined the very optimistic phrase: ...
Frequently, populations, patients and conditions are underrepresented or outright missing in the data. As you know algorithms suffer from a “black box” effect which makes solutions difficult to explain so ...
3 types of data exist: structured unstructured semi-structured Structured data: Until recently written by hand and kept in notebooks, this data is now stored electronically on spreadsheets and databases, and ...
Big data cannot be defined in terms of absolute volume, because it increases so quickly. It is easier to define using the 5 v’s (see below) Moore’s law (1965) The ...
What are some of the challenges, obstacles, and issues that we need to address in realizing this future? Steps in the development of an AI model: To create a model ...
Data is the plural of the Latin word: “datum” The term was first coined by the British cleric Henry Hammond in 1648 but at the time “heaps of data” was ...
Statistical methods in the past could only apply to structured data In the digital age, collection of data to analyze can concern the whole population and no longer needs to ...
In a publication of 2017, 2.5 exabytes (Eb) of data was generated everyday: 1 exabyte 1000 petabytes 1 petabyte is 1000 terabyte That number fits with the statement that more ...
The following article on Digital Health is from Foraus, the Swiss Think tank on Foreign Affairs. The introduction is here below and the full article as well as the webinar ...
In the last 15 to 20 years computing and data power have enabled us to digitize, a complete digital slide. Digital pathology is not only the creation of a digital ...
While building that robust evidence base and showing proof of concept launching pilot studies, how can we move further along to this product in practice implementation of AI ? That ...
Dermatology has been among the first in generating evidence and a robust evidence base for Telehealth to: develop technology and innovation increase access and appropriateness to measure outcomes. However, there ...
Telehealth vs Telemedecine The pandemic has catalyzed the adoption of telehealth. “Telehealth” refers to a broader scope of remote health care services than telemedicine. Telemedicine refers specifically to remote clinical ...
When we think about effective use of Artificial intelligence (AI), we want to be able to measure meaningful outcomes and assess how it actually performs in the real world. This ...
How do we move from our proof of concept then, to a product in practice using AI in dermatology ? For this we need to ask the right questions after ...
The training of a model is influenced by: The amount and quality of Data it receives. The Processing Power: it is more expensive than you think and effective methods are ...
Overfitting Overfitting occurs when the model is accurate with the data it is supplied to predict and the model is quite accurate, so the training set and validation set agree. ...
Explainability is of upmost importance in health in general for explaining the benefit of the patient…and not the least gaining his or her trust. Explainability concerns the algorithmic solution and ...
Even if efforts are made to standardize the image collection (same position), there are distortions which can arise. These can sometimes be corrected but it is important to be aware ...
Taking a standardized clinical picture can help to compare images and enhance machine learning to recognize the conditions, especially when associated with clinical data. It is also of use when ...
The adage goes: garbage in garbage out, and this is especially true for machine learning models, since this data sets on which the models are trained and validated, are essential ...
In the last 15 to 20 years computing and data power have enabled us to digitize, a complete digital slide. Digital pathology is not only the creation of a digital ...
The concept that traditional histology is better than digital pathology is not valid anymore (see: Is Telepathology as Good as Pathology) According to the presenter traditional microscopy, with glass slides ...
Digital pathology, or post 4th industrial revolution pathology has advantages and disadvantages. Pros it’s faster, it’s cheaper, it’s easy access, Second opinion options are easier to get Research is easier ...
People used to argue that digital pathology are not as good as glass histology slides. However, if your technician is good, it doesn’t matter if it is digital or not ...
Making tele-pathology from pathology consists of turning a glass slide into a very high resolution digitized image, It can then be sent over the internet. I. This requires to scan ...
