healthcare

A recent report by McKinsey & Company suggests that generative AI in healthcare has the potential to generate up to $1 trillion in value for the healthcare industry by 2030. This represents a significant opportunity for the healthcare sector, which is constantly seeking new ways to improve patient outcomes, reduce costs, and enhance efficiency. Read more 

However, the integration of generative AI brings both promise and peril. While its potential to revolutionize diagnostics and treatment is undeniable, the risks associated with its implementation cannot be ignored.

Read more about: How AI in healthcare has improved patient care

Let’s delve into the key concerns surrounding the use of generative AI in healthcare and explore pragmatic solutions to mitigate these risks. 

Unmasking the risks: A closer look 

1. Biased outputs:

Generative AI’s prowess is rooted in extensive datasets, but therein lies a potential pitfall – biases. If not meticulously addressed, these biases may infiltrate AI outputs, perpetuating disparities in healthcare, such as racial or gender-based variations in diagnoses and treatments. 

2. False results: 

Despite how sophisticated generative AI is, it is fallible. Inaccuracies and false results may emerge, especially when AI-generated guidance is relied upon without rigorous validation or human oversight, leading to misguided diagnoses, treatments, and medical decisions. 

3. Patient privacy:

The crux of generative AI involves processing copious amounts of sensitive patient data. Without robust protection, the specter of data breaches and unauthorized access looms large, jeopardizing patient privacy and confidentiality. 

4. Overreliance on AI: 

Striking a delicate balance between AI assistance and human expertise is crucial. Overreliance on AI-generated guidance may compromise critical thinking and decision-making, underscoring the need for a harmonious integration of technology and human insight in healthcare delivery. 

5. Ethical considerations

The ethical landscape traversed by generative AI raises pivotal questions. Responsible use, algorithmic transparency, and accountability for AI-generated outcomes demand ethical frameworks and guidelines for conscientious implementation. 

6. Regulatory and legal challenges:

The regulatory landscape for generative AI in healthcare is intricate. Navigating data protection regulations, liability concerns for AI-generated errors, and ensuring transparency in algorithms pose significant legal challenges. 

Read more about: 10 AI startups transforming healthcare

Simple strategies for mitigating the risks of AI in healthcare  

We’ve already talked about the potential pitfalls of AI in healthcare. Hence, there lies a critical need to address these risks and ensure AI’s responsible implementation. This demands a collaborative effort from healthcare organizations, regulatory bodies, and AI developers to mitigate biases, safeguard patient privacy, and uphold ethical principles.  

1. Mitigating biases and ensuring unbiased outcomes  

One of the primary concerns surrounding AI in healthcare is the potential for biased outputs. Generative AI models, if trained on biased datasets, can perpetuate and amplify existing disparities in healthcare, leading to discriminatory outcomes. To address this challenge, healthcare organizations must adopt a multi-pronged approach: 

2. Diversity in data sources:

Diversify the datasets used to train AI models to ensure they represent the broader patient population, encompassing diverse demographics, ethnicities, and socioeconomic backgrounds. 

3. Continuous monitoring and bias detection:

Continuously monitor AI models for potential biases, employing techniques such as fairness testing and bias detection algorithms. 

Human Oversight and Intervention: Implement robust human oversight mechanisms to review AI-generated outputs, ensuring they align with clinical expertise and ethical considerations. 

Safeguarding patient privacy and data security 

The use of AI in healthcare involves the processing of vast amounts of sensitive patient data, including medical records, genetic information, and personal identifiers. Protecting this data from unauthorized access, breaches, and misuse is paramount. Healthcare organizations must prioritize data security by implementing:

Learn about: Top 6 cybersecurity trends

Secure data storage and access controls:

Employ robust data encryption, multi-factor authentication, and access controls to restrict unauthorized access to patient data. 

Data minimization and privacy by design:

Collect and utilize only the minimum necessary data for AI purposes. Embed privacy considerations into the design of AI systems, employing techniques like anonymization and pseudonymization. 

Transparent data handling practices:

Clearly communicate to patients how their data will be used, stored, and protected, obtaining informed consent before utilizing their data in AI models. 

Upholding ethical principles and ensuring accountability 

The integration of AI into healthcare decision-making raises ethical concerns regarding transparency, accountability, and ethical use of AI algorithms. To address these concerns, healthcare organizations must: 

Transparency in AI algorithms:

Provide transparency and explain ability of AI algorithms, enabling healthcare professionals to understand the rationale behind AI-generated decisions. 

Accountability for AI-generated outcomes:

Establish clear accountability mechanisms for AI-generated outcomes, ensuring that there is a process for addressing errors and potential harm. 

Ethical frameworks and guidelines:

Develop and adhere to ethical frameworks and guidelines that govern the responsible use of AI in healthcare, addressing issues such as fairness, non-discrimination, and respect for patient autonomy. 

Ensuring safe passage: A continuous commitment 

The responsible implementation of AI in healthcare requires a proactive and multifaceted approach that addresses potential risks, upholds ethical principles, and safeguards patient privacy.

By adopting these measures, healthcare organizations can harness the power of AI to transform healthcare delivery while ensuring that the benefits of AI are realized in a safe, equitable, and ethical manner. 

Unlocking the Power of LLM Use-Cases: AI applications now excel at summarizing articles, weaving narratives, and sparking conversations, all thanks to advanced large language models.

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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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()

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()

train.describe(include=['object'])

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()

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")

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']))

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:

Predicted prognoses

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.

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.

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:

• Dimensionality Reduction
• Supervised Machine Learning
• Time Series Analysis
• Natural Language Processing

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.