PROMISES OF AI IN MEDICINE By: Wesley Osumo Why is AI the 80% of - - PowerPoint PPT Presentation

PROMISES OF AI IN MEDICINE

By: Wesley Osumo

Why is AI the Future of medicine?

80% of healthcare data is invisible to current systems because it’s unstructured (PWC). 80% of healthcare data is invisible to current systems because it’s unstructured (PWC). Large volumes of Medical data is produced each year.

• For example, for a skin specialist there are 11,000 new dermatology

articles published every year.

Large volumes of Medical data is produced each year.

• For example, for a skin specialist there are 11,000 new dermatology

articles published every year.

Epidemic/Public Health Monitoring

• NYC Macroscope

Epidemic/Public Health Monitoring

• NYC Macroscope

The two types of AI techniques

Machine Learning:

T

• ols that are able to

analyze large sets of images, structured data, and genetic data to make inferences and diagnosis

Natural Language processing:

Machines that are able to read notes and unstructured data to be used in machine learning analysis.

Classical Machine Learning

• Unsupervised learning
• These are datasets that will produce a feature/outcome that

we are not primary aware of.

• Supervised learning:
• using datasets to build a relationship between patient

inputs(health rerecords) and outcomes(stroke, heart attack).

ML in Early detection of strokes

• Over 500 million people worldwide are afected by a stroke each year, and it costs

the global economy $689 billion dollars.

• Villa's ML Algorithms:
• The device monitors movement inside the brain ,if activity is diferent from normal, an Alert

for a stroke is generated. The data is extracted using a Markov model that yields 90.5% accuracy.

• SVM’s can identify strokes from images with 87.6% detection, and can predict

treatment outcomes with 70% certainty.

Deep learning

• Encapsulated neural networks that are layered on top of each other.
• Mostly used for image analysis due to complexity of images.
• Used in Facial recognition , deep analysis, and classifcation problems.
• Convolution Neural Network: Deep learning algorithm used to analyze

visual imagery

• When used for diagnosing cataract disease it yields a 90% accuracy and

treatment.

Natural Language Processing

Useful for extracting unstructured data like textual information(medical and lab notes). Useful for extracting unstructured data like textual information(medical and lab notes). Victor Castro identifed 14 cerebral aneurysms disease-associated variables through implementing NLP on the electronic clinical notes. It was accurate 86% of the time. Victor Castro identifed 14 cerebral aneurysms disease-associated variables through implementing NLP on the electronic clinical notes. It was accurate 86% of the time.

Doctors vs AI Whose more precise

• AI’s precision is between 85%-99% at diagnosing and model prediction.
• https://www.cbsnews.com/video/millions-of-americans-misdiagnosed-every-year/

Soo.. are doctors safe from the AI apocalypse

AI’s can easily replicate this role , and be more accurate. The human ‘face-to-face’ element of healthcare is traditionally cited as vital to healthcare. And yet most doctors probably spend more time going over medical records and interpreting data from tests and other medical interventions than actually spending time with their patients. (PWC) in a cancer research study , 99% of the treatment recommendations from Watson are coherent with the physician decisions.

How do people feel about using AI in medicine?

• PWC survey of individuals

willingness to engage with an AI in medicine

• 54% are willing to engage with

an Ai

• (38%) are unwilling and 7%

neither willing nor unwilling. (PWC)

Efects on healthcare costs

• It’s estimated global healthcare spending is going to reach 18.28 trillion dollars by

2040(WEF).

• How AI will reduce Healthcare costs:
• Reducing the demand of doctors
• Correctly diagnosing
• Early detection
• As treatments are getting better by applying new technologies, these incentives

could be removed by changing to a pay-by-success model.(Samuel Piotr-emch)

Ethical Analysis

• Kantian
• A Kantian philosopher would approve of implementing AI into medicine, but doctors and

healthcare workers must be there to prevent misdiagnosis or biases from the machine.

• algorithms designed to predict patient outcomes using genetics could form a bias from a lack of

information pertaining to certain populations

• Utilitarian:
• The outcome of AI is more accurate and cheaper diagnosis. From a utilitarian perspective AI

in medicine is a no-brainer.

• Negatives: Loss of medical work-force, and security of data.
• As AI techniques improve with time so will the accuracy of these models.

Key promises

• Early detection of diseases
• Autonomous Surgery
• Drug Discovery
• Online quality healthcare delivery
• Accurate prediction of individual health
• More personalized healthcare
• Epidemic monitoring
• Faster drug discovery

References

• Jiang F, Jiang Y, Zhi H, et al Artifcial intelligence in healthcare: past, present and future

Stroke and Vascular Neurology 2017;svn-2017-000101. doi: 10.1136/svn-2017-000101

• Cooper, P

. W. (n.d.). What doctor? Why AI and robotics will defne New Health(Rep.). PWC.https://www.pwc.com/gx/en/industries/healthcare/publications/ai-robotics-new- health/ai-robotics-new-health.pdf

• Global spending on health is expected to increase to $18.28 trillion worldwide by 2040

but many countries will miss important health benchmarks. (n.d.). Retrieved from http://www.healthdata.org/news-release/global-spending-health-expected-increase-1828- trillion-worldwide-2040-many-countries

• Weintraub, A. (2018, March 16). Artifcial Intelligence Is Infltrating Medicine -- But Is It

Ethical? Retrieved from https://www.forbes.com/sites/arleneweintraub/2018/03/16/artifcial-intelligence-is- infltrating-medicine-but-is-it-ethical/#5c53d9a73a24