Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector

How many patients' medical records were uploaded to DeepMind (owned by google) by the NHS without patient consent?

1.6 Million

Why would the method's ability to foretell occurrences beyond the training dataset be diminished if the algorithm achieves extremely high predicted accuracy?

A covariate inside the dataset may have incorrectly referred to the outcome.

What type of shift could help the implementation of machine learning in healthcare?

A shift from treating patients individually to improving healthcare

What is deep learning?

A subset of machine learning that involves training artificial neural networks with multiple layers to learn and identify patterns in data.

What are the main difficulties facing the current healthcare system?

Accessibility, high costs, waste, and an aging population.

What is machine learning?

An artificial intelligence application in which computers are programmed to imitate how humans think and learn.

What purpose does machine learning serve in healthcare?

Assist medical practitioners in patient care and clinical data management.

Why could humans never be as beneficial as AI or algorithms in healthcare?

Because of the unpredictability of many medical processes, humans will never be as orderly or organized as AI would.

Why is clear patient consent important in the use of their information in AI algorithms?

Because patient consent is a key component of data privacy issues and specific patient approval is required to use their information.

What is the main problem that leads to distortion of data used to inform the models?

Biases in the collection of the data.

How can we solve the problem of ever-expanding data sets?

Cloud computing services

What is one thing patients and healthcare givers can do to advance AI?

Create a dialog and discuss benefits and drawbacks alike of AI.

What are some drawbacks with the incorporation of AI into healthcare?

Data collection and algorithm development, ethical concerns, clinical implementation concerns, and social concerns.

What does DL stand for?

Deep learning

what is the so-called "black box" problem?

Deep learning algorithms typically lack the ability to provide convincing explanations for their forecasts and if they are wrong forecasts, they have no way to defend themselves legally or explain to scientists how the data connects to predictions.

What current uses of machine learning are there in healthcare?

Diagnostics, treatment choices, and communication.

Why are businesses hesitant to implement AI-based solutions?

Due to the absence of practical data and the uneven quality of research

The implementation of AI suggests not that employment will be obsolete but instead what?

Employment will need to be reengineered.

What is crucial to developing a solution that can be seamlessly integrated into clinical practice?

Getting input from a wide range of people

What are 2 methods that could help protect patient data yet still help advancements of machine learning?

Improve client-side data encryption, and employ federated learning to train models without data dispersion.

What healthcare concerns did the Coronavirus bring up?

Insufficient protective equipment, insufficient or erroneous diagnostic tests, overworked physicians, and a lack of information exchange.

What does AI do with the racial, gender, and age prejudice which already exists in our society?

It just replicates it in our data.

What is the main obstacle to the successful deployment of AI in healthcare?

Lack of empirical data validating the effectiveness of AI-based medications in planned clinical trials

What can overfitting do?

Lead an AI to make inaccurate predictions.

What does ML stand for?

Machine learning

What AI advancements were made in the wake of the SARS and Ebola pandemics?

More accurate epidemiological forecasting or faster diagnosis

Why is there not a large enough data set for machine learning to work effectively?

Patient records are considered confidential so there is a natural reluctance among institutions to exchange health data.

What is the biggest social concern with the implementation of AI?

People are scared they may lose their jobs.

What can cause biased software and technological artifacts?

Poor design or incorrect or unbalanced data being input into algorithms

What has been the key barrier to successful integration in many examples of innovation adoption?

Stakeholder participation

What phase are a majority of trials incorporating AI into clinical therapy in?

Still in the experimental phases.

What are examples of legislation put into effect to help prevent or limit the collection, use, and sharing of personal information?

The General Computational Regulations of Europe and the Health Research Regulations.

Who is drafting standards to show the effectiveness of AI-driven solutions?

The NHS

What is the downside of protective legislation meant to prevent or limit the collection, use, and sharing of personal information?

The legislation will restrict the quantity of data accessible to train AI systems on a national and global scale.

What is the shift to AI in healthcare being driven by?

The rising healthcare costs and scarcity of educated experts.

What are some methods meant to combat in the AI data sets?

The stereotype neural network and multi-ethnic training sets

Why is the length AI can be used to, widely debated especially in healthcare?

There is no universal guideline for use of AI.

Why are scientists scared that AI will slow down clinicians?

They must be trained to use the AI correctly and it must now slow them down when trying to examine or explore data.

What are the potential goals of AI in healthcare?

To make healthcare more personalized, predictive, preventative, and interactive.

What healthcare concerns did the Coronavirus exacerbate?

Uneven access to treatment, a lack of on-demand services, unreasonably expensive costs, and a lack of price transparency.

What is the difference in a unimodal and a multimodal basis?

Unimodal is a form of machine learning done through only one form such as texts, multimodal is a form of machine learning done through multiple forms such as texts, images, audio etc.

What is one of the problems with more human like AI in healthcare?

Users may mistake artificial systems for people and provide their consent for more covert data collecting.

What is overfitting?

When an algorithm learns unimportant associations between patient features and outcomes