healthcare

10 AI startups revolutionizing healthcare you should know about
Alyshai Nadeem
| August 30, 2022

Healthcare is a necessity for human life, yet many do not have access to it. Here are 10 startups that are using AI to change healthcare.

Healthcare is a necessity that is inaccessible to many across the world. Despite rapid developments and improvements in medical research, healthcare systems have become increasingly unaffordable.

However, multiple startups and tech companies have been trying their best to integrate AI and machine learning for improvements in this sector.

As the population of the planet increases along with life expectancy due to advancements in agriculture, science, medicine, and more, the demand for functioning healthcare systems also rises.

According to McKinsey & Co., by the year 2050, in Europe and North America, 1 in 4 people will be over the age of 65 Source). Healthcare systems by that time will have to manage numerous patients with complex needs.

Read about Top 15 AI startups developing financial services in the USA

Here is a list of a few Artificial Intelligence (AI) startups that are trying their best to revolutionize the healthcare industry as we know it today and help their fellow human beings:

1. Owkin aims to find the right drug for every patient.

owkin logo

Originating in Paris, France, Owkin was launched in 2016 and develops a federated learning AI platform, that helps pharmaceutical companies discover new drugs, enhance the drug development process, and identify the best drug for the ‘right patient.’ Pretty cool, right?

Owkin makes use of different machine learning models to test AI models on distributed data.

The startup also aims to empower researchers across hospitals, educational institutes, and pharmaceutical companies to understand why drug efficacy varies from patient to patient.

Read more about this startup, here.

2. Overjet is providing accurate data for better patient care and disease management.

overjet logo

Founded by PhDs from the Massachusetts Institute of Technology and dentists from Harvard School of Dental Medicine in 2018, Overjet is changing the playground in dental AI.

Overjet makes use of AI to make use of dentist-level understanding of the subject for the identification of diseases and their progression into software.

Overjet aims to provide effective and accurate data to dentists, dental groups, and insurance companies so that they can provide the best patient care and disease management.

You can learn more about the startup, here.

3. From the mid-Atlantic health system to an enterprise-wide AI workforce, Olive AI is improving operational healthcare efficiency.

OliveAI logo

Founded in 2012, Olive AI is the only known AI as a Service (AIaaS) built for the healthcare sector. The premier AI startup utilizes the power of cloud computing by implementing Amazon Web Services (AWS) and automating systems that accelerate time to care.

With more than 200 enterprise customers such as health systems, insurance companies, and a growing number of healthcare companies. Olive AI assists healthcare workers with time-consuming tasks like prior authorizations and patient verifications.

Find out more about Olive AI, click here.

Want to learn more about AI as a Service? Click here.

4. Insitro provides better medicines for patients with the overlap of biology and machine learning.

insitro logo

The perfect cross between biology and machine learning, Insitro aims to support pharmaceutical research and development, and improve healthcare services. Founded in 2018, Insitro promotes Machine Learning-Based Drug Discovery for which it has raised a substantial amount of funding over the years.

According to a recent Forbes ranking of the top 50 AI businesses, the HealthTech startup is ranked at 35 for having the most promising AI-based medication development process.

Further information on Insitro can be found here.

5. Caption Health makes early disease detection easier.

 

caption health

Founded in 2013, Caption Health has since been a top provider of medical artificial intelligence. The startup is responsible for the early identification of illnesses.

Caption Health was the first to provide the FDA-approved AI imaging and guiding software for cardiac ultrasonography. The startup has helped remove numerous barriers to treatment and enabled a wide range of people to perform heart scans of diagnostic quality.

Caption Health can be reached out here.

6. InformAI is trying to transform the way healthcare is delivered and improve patient outcomes.

InformAI logo

Founded in 2017, InformAI expedites medical diagnosis while increasing the productivity of medical professionals.

Focusing on AI and deep learning, as well as business analytics solutions for hospitals and medical companies, InformAI was built for AI-enabled medical image classification, healthcare operations, patient outcome predictors, and much more.

InformAI not only has top-tier medical professionals at its disposal, but also has 10 times more access to proprietary medical datasets, as well as numerous AI toolsets for data augmentation, model optimization, and 3D neural networks.

The startup’s incredible work can be further explored here.

7. Recursion is decoding biology to improve lives across the globe.

recursion logo

A biotechnology startup, Recursion was founded in 2013 and focuses on multiple disciplines, ranging from biology, chemistry, automation, and data science, to even engineering.

Recursion focuses on creating one of the largest and fastest-growing proprietary biological and chemical datasets in the world.

To learn more about the startup, click here

8. Remedy Health provides information and insights for better navigation of the healthcare industry.

Remedy logo

As AI advances, so does the technology that powers it. Another marvelous startup known as Remedy Health is allowing people to conduct phone screening interviews with clinically skilled professionals to help identify hidden chronic conditions.

The startup makes use of virtual consultations, allowing low-cost, non-physician employees to proactively screen patients.

To learn more about Remedy Health, click here.

9. Sensely is transforming conversational AI.

sensely logo

Founded in 2013, Sensely is an avatar and chatbot-based platform that aids insurance plan members and patients.

The startup provides virtual assistance solutions to different enterprises including insurance and pharmaceutical companies, as well as hospitals to help them converse better with their members.

Sensely’s business ideology can further be explored here.

10. Oncora Medical provides a one-stop solution for oncologists.

oncoro medical logo

Another digital health company, founded in 2014, Oncora Medical focuses on creating a crossover between data and machine learning for radiation oncology.

The main aim of the startup was to create a centralized platform for better collection and application of real-world data that can in some way help patients.

Other details on Oncora Medical can be found here.

 

With the international AI in the healthcare market expected to reach over USD 36B by the year 2025, it is only accurate to expect that this market and specific niche will continue to grow even further.

If you would like to learn more about Artificial Intelligence, click here.

Was there any AI-based healthcare startup that we missed? Let us know in the comments below. For similar listicles, click here.

This blog discusses the applications of AI in healthcare. We will learn about some businesses and startups that are using AI to revolutionize the healthcare industry. This advancement in AI has helped in fighting against Covid19.

Introduction:

COVID-19 was first recognized on December 30, 2019, by BlueDot. It did so nine days before the World Health Organization released its alert for coronavirus. How did BlueDot do it? BlueDot used the power of AI and data science to predict and track infectious diseases. It identified an emerging risk of unusual pneumonia happening around a market in Wuhan.

The role of data science and AI in the Healthcare industry is not limited to that. Now, it has become possible to learn the causes of whatever symptoms you are experiencing, such as cough, fever, and body pain, without visiting a doctor and self-treating it at home. Platforms like Ada Health and Sensely can diagnose the symptoms you report.

The Healthcare industry generates 30% of 1.145 trillion MB of data generated every day. This enormous amount of data is the driving force for revolutionizing the industry and bringing convenience to people’s lives.

Applications of Data Science in Healthcare:

1. Prediction and spread of diseases

Predictive analytics process

Predictive analysis, using historical data to find patterns and predict future outcomes, can find the correlation between symptoms, patients’ habits, and diseases to derive meaningful predictions from the data. Here are some examples of how predictive analytics plays a role in improving the quality of life and medical condition of the patients:

  • Magic Box, built by the UNICEF office of innovation, uses real-time data from public sources and private sector partners to generate actionable insights. It provides health workers with disease spread predictions and countermeasures. During the early stage of COVID-19, Magic box correctly predicted which the African States are most likely to see imported cases using airline data. This prediction proved beneficial in planning and strategizing quarantine, travel restrictions, and enforcing social distancing.
  • Another use of analytics in healthcare is AIME. It is an AI platform that helps health professionals in tackling mosquito-borne diseases like dengue. AIME uses data like health center notification of dengue, population density, and water accumulation spots to predict outbreaks in advance with an accuracy of 80%. It aids health professionals in Malaysia, Brazil, and the Philippines. The Penang district of Malaysia saw a cost reduction of USD 500,000 by using AIME.
  • BlueDot is an intelligent platform that warns about the spread of infectious diseases. In 2014, it identified the Ebola outbreak risk in West Africa accurately. It also predicted the spread of the Zika virus in Florida six months before the official reports.
  • Sensely uses data from trusted sources like Mayo Clinic and NHS to diagnose the disease. The patient enters symptoms through a chatbot used for diagnosis. Sensely launched a series of customized COVID-19 screening and education tools with enterprises around the world which played their role in supplying trusted advice urgently.

Want to learn more about predictive analytics? Join our Data Science Bootcamp today.

2. Optimizing clinic performance

According to a survey carried out in January 2020, 85 percent of the respondents working in smart hospitals reported being satisfied with their work compared to 80 percent of the respondents from digital hospitals. Similarly, 74 percent of the respondents from smart hospitals would recommend the medical profession to others, while only 66 percent of the respondents from digital hospitals recommend it.

Staff retention has been a challenge but is now becoming an enormous challenge, especially post-pandemic. For instance, after six months of the COVID-19 outbreak, almost a quarter of care staff quit their job in Flanders & Belgium. The care staff felt exhausted, experienced sleep deprivation, and could not relax properly. A Smart healthcare system can solve these issues.

Smart healthcare systems can help optimize operations and provide prompt service to patients. It forecasts the patient load at a particular time and plans resources to improve patient care. It can optimize clinic staff scheduling and supply, which reduces the waiting time and overall experience.

Getting data from partners and other third-party sources can be beneficial too. Data from various sources can help in process management, real-time monitoring, and operational efficiency. It leads to overall clinic performance optimization. We can perform deep analytics of this data to make predictions for the next 24 hours, which helps the staff focus on delivering care.

3. Data science for medical imaging

According to the World Health Organization (WHO), radiology services are not accessible to two-thirds of the world population. Patients must wait for weeks and travel distances for simple ultrasound scans. One of the foremost uses of data science in the healthcare industry is medical imaging. Data Science is now used to inspect images from X-rays, MRIs, and CT scan to find irregularities. Traditionally, radiologists did this task manually, but it was difficult for them to find microscopic deformities. The patient’s treatment depends highly on insights gained from these images.

Data science can help radiologists with image segmentation to identify different anatomical regions. Applying some image processing techniques like noise reduction & removal, edge detection, image recognition, image enhancement, and reconstruction can also help with inspecting images and gaining insights.

One example of a platform that uses data science for medical imaging is Medo. It provides a fully automated platform that enables quick and accurate imaging evaluations. Medo transforms scans taken from different angles into a 3D model. They compare this 3D model against a database of millions of other scans using machine learning to produce a recommended diagnosis in real-time. Platforms like Medo make radiology services more accessible to the population worldwide.

4. Drug discovery with data science

Traditionally, it took decades to discover a new drug, but the time has now been reduced to less than a year using data science. Drug discovery is a complex task. Pharmaceutical industries rely heavily on data science to develop better drugs. Researchers need to identify the causative agent and understand its characteristics which may require millions of test cases to understand. This is a huge problem for pharmaceutical companies because it can take decades to perform these tests. Data science solved this problem and can perform this task in a month or even a few weeks.

For example, the causative agent for COVID-19 is the SARS-CoV-2 virus. For discovering an effective drug for COVID-19, deep learning is used to identify and design a molecule that binds to SARS-CoV-2 to inhibit its function by using extracted data from scientific literature through NLP (Natural Language Processing).

5. Monitoring patients’ health

The human body generates two terabytes of data daily. Humans are trying to collect most of this data using smart home devices and wearables. The data these devices collect includes heart rate, blood sugar, and even brain activity. Data can revolutionize the healthcare industry if known how to use it.

Every 36 seconds, a person dies from cardiovascular disease in the United States. Data science can identify common conditions and predict disorders by identifying the slightest change in the health indicators. Timely alert of changes in health indicators can save thousands of lives. Personal health coaches are designed to help to gain deep insights into the patient’s health and alert if the health indicator reaches a dangerous level.

Companies like Corti can detect cardiac arrest in 48 seconds through phone calls. This solution uses real-time natural language processing to listen to emergency calls and look out for several verbal and non-verbal patterns of communication. It is trained on a dataset of emergency calls and acts as a personal assistant of the call responder. It helps the responder ask relevant questions, provide insights, and predict if the caller is suffering from cardiac arrest. Corti finds cardiac arrest more accurately and faster than humans.

6. Virtual assistants in healthcare

The WHO estimated that by 2030, the world will need an extra 18 million health workers worldwide. Using virtual assistant platforms can fulfill this need. According to a survey by Nuance, 92% of clinicians believe virtual assistant capabilities would reduce the burden on the care team and patient experience.

Patients can enter their symptoms as input to the platform and ask questions. The platform would tell you about your medical condition using the data of symptoms and causes. It is possible because of the predictive modeling of disease. These platforms can also assist patients in many other ways, like reminding them to take medication on time.

An example of such a platform is Ada Health, an AI-enabled symptom checker. A person enters symptoms through a chatbot, and Ada uses all available data from patients, past medical history, EHR implementation, and other sources to predict a potential health issue. Over 11 million people (about twice the population of Arizona) use this platform.

Other examples of health chatbots are Babylon Health, Sensely, and Florence.

Conclusion:

In this blog, we discussed the applications of AI in healthcare. We learned about some businesses and startups that are using AI to revolutionize the healthcare industry. This advancement in AI has helped in fighting against Covid19. To learn more about data science enroll in our Data Science Bootcamp, a remote instructor-led Bootcamp where you will learn data science through a series of lectures and hands-on exercises. Next, we will be creating a prognosis prediction system in python. You can follow along with my next blog post here.

Follow Along

Want to create data science applications with python? checkout our Python for Data Science training. 

Muhammad Fahad Alam
| July 8, 2022

In this blog, we discussed the applications of AI in healthcare. We took a deep dive into an application of AI, and prognosis prediction using an exercise. We made a simple prognosis detector with an explanation of each step. Our predictor takes symptoms as inputs and predicts the prognosis using a classification model.

Introduction to prognosis prediction

The role of data science and AI (Artificial Intelligence) in the Healthcare industry is not limited to predicting and tracking disease spread. Now, it has become possible to learn the causes of whatever symptoms you are experiencing, such as cough, fever, and body pain, without visiting a doctor and self-treating it at home. Platforms like Ada Health and Sensely can diagnose the symptoms you report.

If you have not already, please go back and read AI & Healthcare. If you have already read it, you will remember I wrote, “Predictive analysis, using historical data to find patterns and predict future outcomes can find the correlation between symptoms, patients’ habits, and diseases to derive meaningful predictions from the data.”

This tutorial will do just that: Predict the prognosis with symptoms as our input.

Exercise: Predict prognosis using symptoms as input

Prognosis Prediction Process
Prognosis Prediction Process

Import required modules

Let us start by importing all the libraries needed in the exercise. We import pandas as we will be reading CSV files as Data Frame. We are importing Label Encoder from sklearn.preprocessing package. Label Encoder is a utility class to convert non-numerical labels to numerical labels. In this exercise, we predict prognosis using symptoms, so it is a classification task.

We are using RandomForestClassifier, which consists of many individual decision trees that work as an ensemble. Learn more about RandomForestClassifier by enrolling in our Data Science Bootcamp, a remote instructor-led Bootcamp. We also require classification reports and accuracy score metrics to measure the model’s performance.

import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, accuracy_score

Read CSV files

We are using this Kaggle dataset for our exercise.

It has two files, Training.csv and Testing.csv, containing training and testing data, respectively. You can download these files by going to the data section of the above link.

Read CSV files into Data Frame using pandas read_csv() function. It reads comma-separated files at supplied file path into DataFrame. It takes a file path as a parameter, so provide the right file path where you have downloaded the files.

train = pd.read_csv("File path of Training.csv")
test = pd.read_csv("File path of Testing.csv")

Check samples of the training dataset

To check what the data looks like, let us grab the first five rows of the DataFrame using the head() function.

We have 133 features. We want to predict prognosis so that it would be our target variable. The rest of the 132 features are symptoms that a person experience. The classifier would use these 132 symptoms feature to predict prognosis.

train.head()
data frame
Head Data frame

The training set holds 4920 samples and 133 features, as shown by the shape attribute of the DataFrame.

train.shape
Output
(4920, 133)

Descriptive analysis

Description of the data in the DataFrame can be seen by describe() method of the DataFrame. We see no missing values in our DataFrame as the count of all the features is 4920, which is also the number of samples in our DataFrame. We also see that all the numeric features are binary and have a value of either 1 or 0.

train.describe()
Describe data frame
Describe data frame
train.describe(include=['object'])
data frame objects
Describe data frame objects

Our target variable prognosis has 41 unique values, so there are 41 diseases in which the model will classify input. There are 120 samples for each unique prognoses in our dataset.

train['prognosis'].value_counts()
Prognosis Column
Value Count of Prognosis Column

There are 132 symptoms in our dataset. The names of the symptoms will be listed if we use this code block.

possible_symptoms = train[train.columns.difference(['prognosis'])].columnsprint(list(possible_symptoms))

Output
['abdominal_pain', 'abnormal_menstruation', 'acidity', 'acute_liver_failure', 'altered_sensorium', 'anxiety', 'back_pain', 'belly_pain', 'blackheads', 'bladder_discomfort', 'blister', 'blood_in_sputum', 'bloody_stool', 'blurred_and_distorted_vision', 'breathlessness', 'brittle_nails', 'bruising', 'burning_micturition', 'chest_pain', 'chills', 'cold_hands_and_feets', 'coma', 'congestion', 'constipation', 'continuous_feel_of_urine', 'continuous_sneezing', 'cough', 'cramps', 'dark_urine', 'dehydration', 'depression', 'diarrhoea', 'dischromic _patches', 'distention_of_abdomen', 'dizziness', 'drying_and_tingling_lips', 'enlarged_thyroid', 'excessive_hunger', 'extra_marital_contacts', 'family_history', 'fast_heart_rate', 'fatigue', 'fluid_overload', 'fluid_overload.1', 'foul_smell_of urine', 'headache', 'high_fever', 'hip_joint_pain', 'history_of_alcohol_consumption', 'increased_appetite', 'indigestion', 'inflammatory_nails', 'internal_itching', 'irregular_sugar_level', 'irritability', 'irritation_in_anus', 'itching', 'joint_pain', 'knee_pain', 'lack_of_concentration', 'lethargy', 'loss_of_appetite', 'loss_of_balance', 'loss_of_smell', 'malaise', 'mild_fever', 'mood_swings', 'movement_stiffness', 'mucoid_sputum', 'muscle_pain', 'muscle_wasting', 'muscle_weakness', 'nausea', 'neck_pain', 'nodal_skin_eruptions', 'obesity', 'pain_behind_the_eyes', 'pain_during_bowel_movements', 'pain_in_anal_region', 'painful_walking', 'palpitations', 'passage_of_gases', 'patches_in_throat', 'phlegm', 'polyuria', 'prominent_veins_on_calf', 'puffy_face_and_eyes', 'pus_filled_pimples', 'receiving_blood_transfusion', 'receiving_unsterile_injections', 'red_sore_around_nose', 'red_spots_over_body', 'redness_of_eyes', 'restlessness', 'runny_nose', 'rusty_sputum', 'scurring', 'shivering', 'silver_like_dusting', 'sinus_pressure', 'skin_peeling', 'skin_rash', 'slurred_speech', 'small_dents_in_nails', 'spinning_movements', 'spotting_ urination', 'stiff_neck', 'stomach_bleeding', 'stomach_pain', 'sunken_eyes', 'sweating', 'swelled_lymph_nodes', 'swelling_joints', 'swelling_of_stomach', 'swollen_blood_vessels', 'swollen_extremeties', 'swollen_legs', 'throat_irritation', 'toxic_look_(typhos)', 'ulcers_on_tongue', 'unsteadiness', 'visual_disturbances', 'vomiting', 'watering_from_eyes', 'weakness_in_limbs', 'weakness_of_one_body_side', 'weight_gain', 'weight_loss', 'yellow_crust_ooze', 'yellow_urine', 'yellowing_of_eyes', 'yellowish_skin']

There are 41 unique prognoses in our dataset. The name of all prognoses will be listed if we use this code block:

list(train['prognosis'].unique())
Output
['Fungal infection','Allergy','GERD','Chronic cholestasis','Drug Reaction','Peptic ulcer diseae','AIDS','Diabetes ','Gastroenteritis','Bronchial Asthma','Hypertension ','Migraine','Cervical spondylosis','Paralysis (brain hemorrhage)','Jaundice','Malaria','Chicken pox','Dengue','Typhoid','hepatitis A','Hepatitis B','Hepatitis C','Hepatitis D','Hepatitis E','Alcoholic hepatitis','Tuberculosis','Common Cold','Pneumonia','Dimorphic hemmorhoids(piles)','Heart attack','Varicose veins','Hypothyroidism','Hyperthyroidism','Hypoglycemia','Osteoarthristis','Arthritis','(vertigo) Paroymsal  Positional Vertigo','Acne','Urinary tract infection','Psoriasis','Impetigo']

Data visualization

new_df = train[train.columns.difference(['prognosis'])]
#Maximum Symptoms present for a Prognosis are 17
new_df.sum(axis=1).max()
Minimum Symptoms present for a Prognosis are 3
new_df.sum(axis=1).min()
series = new_df.sum(axis=0).nlargest(n=15)
pd.DataFrame(series, columns=["Occurance"]).loc[::-1, :].plot(kind="barh")
bar chart
Horizontal bar chart for Occurrence column

Fatigue and vomiting are the symptoms most often seen.

Encode object prognosis

Our target variable is categorical features. Let us create an instance of Label Encoder and fit it with the prognosis column of train data and test data. It will encode all possible categorical values in numerical values.

label_encoder = LabelEncoder()
label_encoder.fit(pd.concat([train['prognosis'], test['prognosis']]))

It concludes the data preparation step. Now, we can move on to model training with this data.

Training and evaluating model

Let us train a RandomForestClassifier with the prepared data. We initialize RandomForestClassifier, fit the features and label in it then finally make a prediction on our test data.

In the end, we transform label encoded prognosis values back to the original form using the fit_transform() method of the LabelEncoder object.

random_forest = RandomForestClassifier()
random_forest.fit(train[train.columns.difference(['prognosis'])], label_encoder.fit_transform(train['prognosis']))
y_pred = random_forest.predict(test[test.columns.difference(['prognosis'])])
y_true = label_encoder.fit_transform(test['prognosis'])
print("Accuracy:", accuracy_score(y_true, y_pred))
print(classification_report(y_true, y_pred, target_names=test['prognosis']))
Classification report
Classification report

Predict prognosis by taking symptoms as input

We have our model trained and ready to make predictions. We need to create a function that takes symptoms as input and predicts the prognosis as output. The function predict_prognosis() below is just doing that.

We take input features as a string of symptoms separated by space. We strip the string to remove spaces at the beginning and end of the string. We split this string and created a list of symptoms. We cannot use this list directly in the model for prediction as it contains symptoms’ names, but our model takes a list of 0 and 1 for the absence and presence of symptoms. Finally, with the features in the desired form, we predict the prognosis and print the predicted prognosis.

def predict_prognosis():
  print("List of possible Symptoms you can enter: ", list(train[train.columns.difference(['prognosis'])].columns))
  input_symptoms = list(input("\nEnter symptoms space separated: ").strip().split())
  print(input_symptoms)
  test_value = []
  for symptom in train[train.columns.difference(['prognosis'])].columns:
    if symptom in input_symptoms:
      test_value.append(1)
    else:
      test_value.append(0)
    np_test = np.array(test_value).reshape(1, -1)
    encoded_label = random_forest.predict(np_test)
  predicted_label = label_encoder.inverse_transform(encoded_label)[0]
  print("Predicted Prognosis: ", predicted_label)
predict_prognosis()

Give input symptoms:

Input Symptoms | Data Science Dojo

Predicted prognoses

Suppose we have these symptoms abdominal pain, acidity, anxiety, and fatigue. To predict prognosis, we must enter the symptoms in comma separate fashion. The system will separate the symptoms, transform them into a form model that can predict and finally output the prognosis.
Output prognosis
Output prognosis

Conclusion

To sum up, we discussed the applications of AI in healthcare. Took a deep dive into an application of AI, and prognosis prediction using an exercise. Created a prognosis predictor with an explanation of each step. Finally, we tested our predictor by giving it input symptoms and got the prognosis as output.

Full Code Available!

Herman 'HP' Morgan
| June 30, 2019

Data science and medicine working together is the next big step for healthcare. It, therefore, makes sense that doctors must have some knowledge of data science.

We have entered into the era of big data where we are not only using every bit of data originating from every source but also making smart decisions that accelerate business growth. No matter what industry you’re in, AI & Big Data are all the rage these days, and the need for storing data has grown over the years. The following post emphasizes why healthcare professionals should learn data science.

It’s said, Health is wealth; with great Health, you can conquer it all! The medicine and healthcare industries are considered as one of the most revolutionary and promising industries around. Slowly and steadily things are changing from computerizing medical records to drug discovery, and genetic disease exploration; one can find data analytics moving medical science to a whole new level. And trust me, the fun has just begun!

Healthcare and data science

Healthcare and data science are often linked as increased industries tend to attempt to reduce their expenses with the help of data. Day in, day out, the field of data science in medicine is developing on a rapid basis and it’s important they keep on marching together.

In general, data scientists are the ones who have advanced training in statistics, math, and computer science. Data visualization, data mining, and information management are some of its crucial aspects.

doctor tablet data science
An X-Ray showing multiple stats (Source: Forbes)
Businesses have already jumped at the opportunity produced by the combination of data science and healthcare. For example, Omada Health developed a program aimed at reducing the risk of preventable health issues. It takes data from smart devices, like scales and pedometers, to process the patient’s behavioral data, and develops a highly customized program based on the results.

Another Example is produced by Enlitic, an organization that pairs radiologists with data scientists to increase the accuracy and efficiency of diagnostics. Featuring deep learning algorithms, data scientists help analyze data from CT scans, X-rays, etc. so that doctors can “diagnose sooner with renowned accuracy”.

A few skills required to make out a career as a healthcare data scientist include:

Furthermore, I would like to mention some interesting use cases of data science with the highest impact and the most significant potential for the future in the healthcare realm.

  1. Medical image analysis – If we look at the overall healthcare sector, you will find that it has received a great bunch of benefits from data science applications. Several techniques, including magnetic resonance imaging (MRI), X-ray, computed tomography, and mammography, are used for better treatment.
  2. Whether it’s about improving the image quality or extracting data from images more efficiently and providing the most accurate interpretation, data science seems to, and will continue to, contribute to a great extent.
  3. Genetics and Genomics – This feature has enabled an advanced level of treatment personalization.
  4. Professionals need to understand the impact of DNA on our Health and find individual biological connections between genetics, diseases, and drug response. Data science techniques allow for the integration of various kinds of data with genomic data in disease research. This provides a deeper understanding of how different genetic structures will react to drugs and diseases.
  5. Virtual assistance – Optimization of the clinical process builds upon the concept that, for many cases, it is not actually necessary for patients to visit doctors in person. The mobile application can give a more effective solution by “bringing the doctor to the patient”.  AI-powered mobile apps can provide basic healthcare support, usually as chatbots.
  6. All you have to do is describe your symptoms, or ask questions, and then receive key information about your medical condition. Apps can remind you to take your medicine on time, and if necessary, assign an appointment with a doctor.

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