Incorporating Artificial Intelligence into the Healthcare Industry

Incorporating Artificial Intelligence into the Healthcare Industry

Shittu Olumide Ayodeji's photo
Shittu Olumide Ayodeji
·

10 min read

Despite the reality that Artificial Intelligence invokes worry in a maximum of us, it's far reaping rewards us in several ways. Artificial Intelligence In Healthcare is revolutionizing the scientific enterprise via way of means of supplying an assisting hand. This weblog will assist you to apprehend the high-quality effect of Artificial Intelligence withinside the healthcare domain.

Here’s a listing of subjects that I’ll be protecting in this article:

  • What Is Artificial Intelligence?
  • Artificial Intelligence In Healthcare
  • What Is Machine Learning?
  • What Is Deep Learning?
  • Hands-On
  • What Is Artificial Intelligence?

Artificial Intelligence is a manner of the use of Machine Learning, Deep Learning, Natural language Processing, and plenty of different strategies to construct artificially smart fashions that may carry out excessive-stage computations and resolve complicated issues.

Now let’s apprehend how AI is impacting healthcare.

Artificial Intelligence In Healthcare

Since the creation of Artificial Intelligence withinside the 1950s, it's been impacting diverse domain names which include marketing, finance, the gaming enterprise, or even the musical arts. However, the biggest effect of Artificial Intelligence is withinside the subject of Healthcare. According to the contemporary document via way of means of PwC, AI will make contributions an additional $15.7 trillion to the arena financial system via way of means of 2030, and the finest effect could be withinside the subject of healthcare.

In the beneath phase, you'll apprehend how AI is getting used to resolve actual-international use cases.

Artificial Intelligence In Data Management

Artificial Intelligence is reaping rewards healthcare businesses via way of means of enforcing cognitive era to unwind a massive quantity of scientific data and carry out strength prognosis. Take, for instance, Nuance the prediction provider issuer that makes use of Artificial Intelligence and Machine Learning to prescient the motive of customers.

Artificial Intelligence In Managing Medical Data

By enforcing Nuance in an organization’s workflow you may increase a customized consumer enjoy that allows an organization to take higher movements that beautify the client’s enjoy and usual blessings business.

Here’s a listing of key functions of Nuance:

  • Service acceleration: Suggest the great subsequent step to make sure that the consumer’s desires are met.

  • Call deflection: Minimize the number of inbound name volumes and decrease the charges via way of means of expecting the client's motive and diverting clients to different online engagements.

  • Churn reduction: Using Machine Learning and Natural Language Processing strategies to expect the conduct of leads that can be near invalidating their provider primarily based totally on their history, searches, sentiments, etc, and make the suitable movement to keep away from such provider cancellations

  • Automate tedious tasks: Eradicate the monotonous assignment of calling customers via way of means of enforcing an automatic machine that sends notifications thru SMS or email and makes use of AI-primarily based totally chatbots that make matters less difficult. Now let’s speak about how Artificial Intelligence is the use of Deep Learning strategies to enhance scientific prognosis.

Artificial Intelligence In Medical Diagnosis

“Medical imaging and prognosis powered via way of means of AI must witness extra than 40% increase to surpass USD 2.5 billion via way of means of 2024.” – Global Market Insights. With the assist of Neural Networks and Deep mastering fashions, Artificial Intelligence is revolutionizing the photo prognosis subject in medicine. It has taken over the complicated evaluation of MRI scans and made it a less difficult manner.

Artificial Intelligence In Medical Diagnosis

MRI scans are tough to research because of the number of records they contain. A regular MRI evaluation takes numerous hours and researchers looking to formulate final results from massive facts units, look forward to hours for a pc to generate the scans. Large and complicated facts units may be analyzed with the assist of neural networks and that is precisely what a group of researchers applied in MIT. They advanced a neural community known as VoxelMorph that become educated on facts set of approx 7000 MRI scans. A neural community features via way of means of inputting facts at one cease which undergoes a metamorphosis during the community till the very last favored output is formed. Neural networks paintings at the precept of weights and bias.

Artificial Intelligence In Early Detection

Artificial Intelligence has performed a key position withinside the early prediction of scientific situations consisting of coronary heart attacks. Many AI-primarily based totally wearable fitness trackers were advanced to reveal the fitness of someone and show warnings whilst the tool collects something uncommon or unlikely. Examples of such wearables consist of Fitbit, Apple watch, and plenty of others.

Artificial Intelligence In Early Prediction

‘Precaution is usually higher than cure’, that is the motto at the back of the contemporary launch of the Apple watch.

Apple used Artificial Intelligence to construct an eye fixed that video display units a person’s fitness.

This watch collects facts consisting of someone’s coronary heart rate, sleep cycle, respiration rate, interest stage, blood strain, etc., and maintains a file of those measures 24/7.

These accrued facts are then processed and analyzed via way of means the use of Machine Learning and Deep mastering algorithms to construct a version that predicts the hazard of a coronary heart attack.

Thanks to the Apple watch, a person named Scott Killian stored his life.

Artificial Intelligence In Medical Assistance

As the want for scientific help has grown, the improvement of AI-primarily based totally digital nurses has increased. According to a current survey, Virtual nursing assistants correspond to the most near-time period fee of USD 20 billion via way of means of 2027.

Sensely is one such instance of a digital nurse that implements Natural Language Processing, speech recognition, Machine Learning, and wi-fi integration with scientific gadgets consisting of blood strain cuffs to offer scientific help to patients.

Artificial Intelligence In Medical Assistance

Here’s a listing of key functions that the digital nurse, Sensely provides:

  • Self-care
  • Clinical advice
  • Scheduling an appointment
  • Nurse Line
  • ER Direction

With such revolutions withinside the subject of healthcare, it's far clean that notwithstanding the dangers and the so-known as ‘threats’, Artificial Intelligence is reaping rewards us in lots of ways.

Artificial Intelligence In Decision Making

Artificial Intelligence has performed the main position in selection-making. Not most effective withinside the healthcare enterprise however AI has additionally progressed agencies via way of means of reading client desires and comparing any capability dangers.

An effective use case of Artificial Intelligence in selection making is the usage of surgical robots that may decrease mistakes and versions and subsequently assist in growing the performance of surgeons. One such surgical robotic is the Da Vinci, pretty aptly named, which lets in expert surgeons enforce complicated surgical procedures with higher flexibility and manipulation than traditional approaches.

Artificial Intelligence In Decision Making

Key functions of the Da Vinci consist of:

Aiding surgeons with a complicated set of instruments Translating the surgeon’s hand actions on the console in actual time Producing clean and magnified, 3-D excessive-definition photo of the surgical area Surgical robots are now no longer the most effective help in selection-making processes, however, additionally, they enhance the general overall performance via way of means of growing accuracy and performance.

So the ones had been multiple actual-international programs of Artificial Intelligence in healthcare. Throughout the weblog, I’ve noted very vital fields of AI, Machine Learning, and Deep Learning. Let’s apprehend what precisely those phrases mean.

What Is Machine Learning?

Machine Learning is the manner of feeding machines heaps of facts to be able to interpret, manner, and examine this fact which will produce actionable insights that advantage an organization.

What Is Deep Learning?

Deep Learning is an extra superior subject of Machine Learning that makes use of the idea of Neural Networks to resolve extra convoluted issues that require excessive dimensional facts and automatic characteristic extraction.

Now let’s study how a use case of Artificial Intelligence in healthcare may be applied via way of means of the use of Deep Learning concepts.

Deep Learning With Python

A quick disclaimer earlier than we get into the hands-on part:

I’ll be using Python to run this demo, so in case you don’t understand the language, right here are multiple blogs to get you commenced with Python Programming:

  • Python Tutorial – A Complete Guide to Learn Python Programming

  • How to Learn Python three from Scratch – A Beginners Guide

  • Python Programming Language – Headstart With Python Basics

  • A Beginners Guide To Python Functions

Problem Statement: To Study the Breast Cancer Wisconsin (Diagnostic) Data Set and version a Neural Network classifier that predicts the degree of Breast Cancer as both M (Malignant) or B (Benign).

Data Set Description: The facts set carries descriptive records of approximately the molecular nucleus found in a pattern. It carries around 32 attributes/ functions so as to assist in classifying whether or not a specific pattern is cancerous or now no longer. You can locate the facts set right here.

Logic: To construct a binary neural community that may classify a molecular pattern efficaciously as both cancerous or now no longer. The output produced could be an express variable that carries values:

  • Malignant – Cancerous cells

  • Benign – Non-cancerous cells

Now which you understand the common sense at the back of the trouble statement, it’s time to place to your detective glasses and begin coding. Step 1: Import the required packages

# Linear algebra

import numpy as np

# Data processing
import pandas as pd

import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt2

from sklearn import preprocessing
from subprocess import check_output

Step 2: Read the input data

# Import the data set
data = pd.read_csv('C://Users//NeelTemp//Desktop//data.csv')

#Display the first few observations in the data set

print(data.head())

         id diagnosis  ...  fractal_dimension_worst  Unnamed: 32
0    842302         M  ...                  0.11890          NaN
1    842517         M  ...                  0.08902          NaN
2  84300903         M  ...                  0.08758          NaN
3  84348301         M  ...                  0.17300          NaN
4  84358402         M  ...                  0.07678          NaN

[5 rows x 33 columns]

Step 3: Data Processing

# Cleaning and modifying the data
data = data.drop('id',axis=1)
data = data.drop('Unnamed: 32',axis=1)

# Mapping Benign to 0 and Malignant to 1
data['diagnosis'] = data['diagnosis'].map({'M':1,'B':0})

# Scaling the dataset
datas = pd.DataFrame(preprocessing.scale(data.iloc[:,1:32]))
datas.columns = list(data.iloc[:,1:32].columns)
datas['diagnosis'] = data['diagnosis']

# Creating the high dimensional feature space X
data_drop = datas.drop('diagnosis',axis=1)
X = data_drop.values

Step 4: Building a neural network

# Create a feed forward neural network with 3 hidden layers
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Input
from keras.optimizers import SGD

model = Sequential()
model.add(Dense(128,activation="relu",input_dim = np.shape(X)[1]))
model.add(Dropout(0.25))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(32, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])

Step 5: Data Splicing and Cross Validation

# Fit and test the model by randomly splitting it
# 67% of the data for training and 33% of the data for validation
model.fit(X, datas['diagnosis'], batch_size=5, epochs=10,validation_split=0.33)

# Cross validation analysis
from sklearn.model_selection import StratifiedKFold

# K fold cross validation (k=2)
k = 2
kfold = StratifiedKFold(n_splits=2, shuffle=True, random_state=seed)
cvscores = []
Y = datas['diagnosis']
for train, test in kfold.split(X, Y):

Step 6: Running the model

# Fit the model
model.fit(X[train], Y[train], epochs=10, batch_size=10, verbose=0)

Train on 381 samples, validate on 188 samples
Epoch 1/10

5/381 [..............................] - ETA: 2:39 - loss: 0.5185 - acc: 0.8000
45/381 [==>...........................] - ETA: 16s - loss: 0.6274 - acc: 0.6444
100/381 [======>.......................] - ETA: 6s - loss: 0.5755 - acc: 0.7100
155/381 [===========>..................] - ETA: 3s - loss: 0.4560 - acc: 0.7871
215/381 [===============>..............] - ETA: 1s - loss: 0.3723 - acc: 0.8326
260/381 [===================>..........] - ETA: 1s - loss: 0.3404 - acc: 0.8538
305/381 [=======================>......] - ETA: 0s - loss: 0.3252 - acc: 0.8623
381/381 [==============================] - 3s 7ms/step - loss: 0.2802 - acc: 0.8845 - val_loss: 0.0870 - val_acc: 0.9628

Epoch 2/10

5/381 [..............................] - ETA: 0s - loss: 0.0647 - acc: 1.0000
165/381 [===========>..................] - ETA: 0s - loss: 0.0966 - acc: 0.9758
381/381 [==============================] - 0s 314us/step - loss: 0.0944 - acc: 0.9659 - val_loss: 0.0497 - val_acc: 0.9894

Step 7: Model Evaluation

# evaluate the model
scores = model.evaluate(X[test], Y[test], verbose=0)

# Print scores from each cross validation run
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

acc: 97.89%
acc: 97.89%

cvscores.append(scores[1] * 100)
print("%d-fold cross validation accuracy - %.2f%% (+/- %.2f%%)" % (k,np.mean(cvscores), np.std(cvscores)))

2-fold cross validation accuracy - 97.89% (+/- 0.00%)

As you can see, the Neural Network achieves a score of 97.89 percent accuracy, which is rather impressive. You may use parameter tweaking and optimization approaches like the drop-off method with a more efficient value if you want to optimize the model even further.

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