AI and Sensors are a perfect match today. One supplies the data, the other one acts on the data. Here is what you need to know...

In the last 25 years computers and Internet have made worldwide communication and information access (virtually) free and instantaneous for everyone on the planet. Similarly, I predict that AI will enable expert medical diagnosis - another critical need for everyone - will become free and instantaneous for everyone. Next step in Healthcare is a personalized treatment-plan. AI has shown great promise in formulating treatment-plans based on patient medical data and millions of cases with comparable history.

AI is now used in B2C and also in B2B applications. What works best, what are some examples and differences, where should you focus?

We spent an afternoon with David Jenkins from eXXcellent Solutions (https://www.exxcellent.de) talking to the robot Pepper. We interviewed him (or her) on our podcast on Artificial Intelligence. Meet Pepper!

AI in Healthcare

100 years ago medical knowledge doubled every 150 years.

Now it doubles every 73 days.

800,000 journal articles are published every year.

Human genome has 3 billion data points.

We need AI just to keep up.

http://fortune.com/2018/08/22/toby-cosgrove-cleveland-clinic-brainstorm-health/

Some big healthcare challenges with a promise of benefiting from AI and automation:

● Diagnosis and Treatment Plan

○ Fast with expert level accuracy

○ Economical and accessible for all

● Patient Remote Monitoring With Emergency Detect

● Elderly Care

● Detection and spread of new infections

● Precision medicine

● Clinical Trail Eligibility Assessment

● Drug Discovery

○ Faster and Cheaper

Healthcare costs have been rising steeply for many years coupled with a severe shortage of professionals like doctors, nurses and caretakers in most developed countries. Chronic illnesses, as opposed to infectious diseases, need a long-term management of health for a population with growing number of elderly people. Most important need in healthcare today is quick, cheap, accurate, readily accessible expert level diagnosis of most common current illnesses. Use of AI and robotic technology for automating diagnosis and medical procedures seems the only way out to satisfy healthcare needs.

Details on some new methods of diagnosis leveraging AI:

Skin Cancer.

● http://news.stanford.edu/2017/01/25/artificial-intelligence-used-identify-skin-cancer/

● https://www.theverge.com/2017/1/26/14396500/ai-skin-cancer-detection-stanford-university

● Google’s image recognition AI was used as a starting point

● 130,000 validated samples used for training AI

● Covers over 2000 skin diseases – incl. Melanoma

● 2000 gold standard used for testing AI vs. 21 expert dermatologists – AI achieved 91% accuracy

● With melanomas, for example, the human dermatologists correctly identified 95 percent of malignant lesions and 76 percent of benign moles. In the same tests, the AI was correct 96 percent of the time for the malignant samples, and 90 percent of the time for harmless lesions.

● 5.4 million new cases of skin cancer diagnosed in the US every year

● Chances of survival 97% for early diagnosis and treatment but falls to 14% in 5 years.

AI to predict cardiovascular risks – from retina image.

• Google scientists trained AI with a medical dataset and eye scans of nearly 300,000 patients.

• Rear interior wall of the eye (the fundus) is chock-full of blood vessels that reflect the body’s overall health.

• Accurately deduce an individual’s age, sex, blood pressure, BMI, Diabetic, and whether they smoke.

• AI predicts risk of suffering a major cardiac event.

• https://www.theverge.com/2018/2/19/17027902/google-verily-ai-algorithm-eye-scan-heart-disease-cardiovascular-risk

• https://www.nature.com/articles/s41551-018-0195-0#Fig2

• https://www.nature.com/articles/s41551-018-0195-0/figures/2

AI to diagnose Parkinson’s – in just 3 minutes

• At King’s College Hospital, London, with Tencent and Medopad

• Patients need to wear no sensors or devices – no hospital visit needed

• Motor function assessment – normally takes half hour by professional

• 10 million people worldwide live with Parkinson’s

• https://www.bbc.com/news/technology-45760649/

AI to spot early signs of Alzheimer’s – before your family does

• Wireless tracking thousands of movements every day looking for subtle changes in behavior and sleep patterns:

• Sleeping or fallen

• Gait speed

• Sleep patterns

• Location

• Breathing pattern

• MIT’s AI Machine Learning predicts the symptoms of Alzheimer’s. Not obvious in the early stages.

• https://www.technologyreview.com/s/609236/ai-can-spot-signs-of-alzheimers-before-your-family-does

• Wireless tracking with AI: http://news.mit.edu/2018/artificial-intelligence-senses-people-through-walls-0612

Positive

• All products and services improve with automation

o More abundant

o Easier to use

o More personalized

o Faster

o Cheaper

• “Experts at your service 24x7” – via your smartphone and other devices

o Finance

o Medical

o Law

o Taxation

o …

Negative

AI can create major psychological and sociological impact on individuals and society leading to disorientation (for some) due to changes in:

• Privacy

• Security

• Job loss and insecurity

• Burden of learning new ways of doing things

• Inequality

• Loss of meaning and purpose

What are the most pressing questions that business have on AI? We do a lot of presentations, here is what we get asked and what you should know...

AI&U Episode 10 The AI Canvas by AI&U - Sharad Gandhi and Christian Ehl

Can you think of an industry that does not benefit from intelligence? We believe that AI can enhance all business areas and will fundamentally impact most industries and businesses. The reason is very simple  —  artificial intelligence allows you to make better decisions for both simple and very complex tasks. It does so by understanding and evaluating all the parameters and factors that influence it. It can leverage complete sets of data and it can better understand the influencing factors and produce an answer that is more reliable than humans alone can. We are seeing the evolution of new AI-based tools and services, that can help organizations function more competitively, optimize core business processes, as well as create better products and services for customers. Continuous technological advancement in computing power, connectivity, neural networks will fuel the development of more and better AI, which can be leveraged in business.

In this podcast we discuss how to approach the value of AI for business.

The quality of the AI is defined by the data that trains it. Any bias in the training data can affect the training of the ML algorithms and ultimately the accuracy of the AI. With human in the loop for algorithm training, tuning and testing, in addition to ensuring that the AI is working as it was intended to, it also serves as a safeguard against bias as human judges can make ethical, cultural and emotional judgements that an AI shouldn’t be tasked or not be trusted to do.

However, this introduces another challenge, the implicit bias. The biases people involved in the development of an AI may have, could be inadvertently transferred to the decision making of the AI they helped develop or train.

What is considered fair and acceptable in a society also changes over time. Predictive models learning from historical data pose a serious problem when they are being used in high-stake situations.

AI powered systems can amplify biases in society, not just individuals. A sophisticated AI powered system, drawing from a historical database for it decision making, maybe blind to bias caused by patterns reflecting centuries old discrimination. Data that seems neutral may have correlations embedded in them that could lead deep learning programs to make decisions that are biased to minorities or under-represented groups.

Sampling-bias problem can cause image recognition programs to “ignore” under-represented groups in data. Example of popular data sets used to train image recognition AI included gender-bias where a picture of man cooking would be misidentified as a woman or race-bias where lighter-skinned candidates were deemed more beautiful as they represented the majority. Based on common characteristics of majority of highest ranking corporate executives, an AI program tasked to pick out perfect candidates for high ranking roles may consider white males to fit the bill better. Oversampling can be used to mitigate some of this challenge by assigning heavier statistical weights to underrepresented data.

Mostly the members of a dominant majority are oblivious to the experiences of the other group. To fight bias, we need to have more diversity. Diversity in humans in the loop of an AI will ensure that no viewpoint is ignored. And as AI amplifies and brings to light more intrinsic biases in our society, we need to start expecting better from our society as we do from AI.

Philosophical question (and might be slight digression from the topic at hand): Do we expect AI to behave more ethically than most humans? If the task of an AI is to “do what humans can do”, can we humans “oversample” in our mind when we are making decisions? I belong to a minority group. If I want an AI program to predict my professional success, would I not want it to take in account all the racial and gender biases that will affect by career in real world? Maybe over time social values evolve to a point where human decisions/actions become gender neutral and bias-free, but for now do I expect more from a machine than I do from the society I currently live in? Should the an AI’s decisions represent where the society stands today or where it should stand in an ideal world?

Thank you Sara for being the guest on this show! If you have any question for Sara - please contact her at [email protected].