Deep Learning in Healthcare: 12 Real-World Applications
The computing capabilities of deep learning models enable fast, accurate, and efficient operations in patient care, R&D, and insurance.
Key deep learning in healthcare includes:
IDC claims that:
- Research in the pharma industry is one of the fastest-growing use cases
- Global spending on AI will be more than $110 billion in 2024
Patient Care
1. Patient Check-Ins
Hippocratic AI released a number of LLM-powered nurse personas to check in with patients
- before operations (e.g., colonoscopy) to ensure that they are well prepared
- after operations, as they adjust to their daily life
- dealing with chronic diseases
The company shares on its website that tens of hospitals in the US are testing the software.1
Such deep learning-based chatbots can answer questions of healthcare professionals or patients themselves.
2. Medical imaging
Deep learning powers image recognition and object detection for MRIs, CT scans, other imaging modalities, supporting:
- Image segmentation
- Disease detection
- Prognosis prediction
Example applications:
- Diabetic retinopathy detection
- Early Alzheimer’s diagnosis
- Ultrasound breast nodule analysis
Google DeepMind: Google’s DeepMind has been collaborating with healthcare institutions to create AI models for medical imaging.
In one notable case, DeepMind developed a model to detect over 50 different eye diseases using retinal scans, assisting healthcare professionals in diagnosing conditions such as diabetic retinopathy and macular degeneration at earlier stages2 .
Figure 1: AI system for predicting exAMD.
3. Healthcare data analytics
Deep learning models analyze electronic health records (EHR) containing structured and unstructured data: clinical notes, laboratory test results, diagnoses, medications at exceptional speeds with highest possible accuracy.
Wearable devices: Smartphones and wearable devices provide useful information about lifestyle. Potential to transform data by using mobile apps to monitor medical risk factors.
FDA approval: In 2019, Current Health’s AI wearable device became one of the first AI medical monitoring wearables approved by the Food and Drug Administration (FDA) for home use.
Who uses this: Hospitals analyzing patient data at scale, healthcare systems optimizing care pathways, and researchers studying population health trends.
Feel free to read our examples on Healthcare Analytics for more.
4. Mental health chatbots
AI-powered mental health apps such as Happify, Moodkit, Woebot, and Wysa use deep learning to create more realistic, supportive interactions.
Real-life example:
Woebot is an AI-powered mental health chatbot using natural language processing and deep learning to have conversations with users, providing cognitive behavioral therapy (CBT) techniques to help reduce anxiety and depression symptoms.3 .
5. Personalized medical treatments
Deep learning solutions enable healthcare organizations to deliver personalized patient care by analyzing patients’ medical histories, symptoms, test results. Natural language processing (NLP) provides insights from free-text medical information for most relevant medical treatments.
Real-life example:
IBM Watson utilizes deep learning and natural language processing (NLP) to analyze vast amounts of medical literature, patient data, and treatment protocols, suggesting personalized cancer treatment options. Watson cross-references patients’ genetic profiles and medical histories with the latest oncology research to recommend the most effective treatment plans for individual patients4 .
6. Prescription audit
Deep learning models audit prescriptions against patient health records to identify and correct potential diagnostic or prescribing errors.
Real-life Example:
Frost & Sullivan has reported that AI-driven systems, including those utilizing deep learning in healthcare, are employed by hospitals and insurance companies in prescription audits to detect medication errors. These systems cross-check prescriptions against patient health records to flag any inconsistencies or potential harm due to incorrect medications.
Health Insurance
7. Underwriting
Deep learning models help insurance companies make offers to customers through powerful predictive analytics.
Real-life Example:
Lemonade uses AI and deep learning models to optimize its underwriting processes. By analyzing customer data and predicting risk more effectively, the company offers more accurate and faster insurance policies, reducing the need for human involvement5 .
8. Fraud detection
Models identify fraudulent claims by analyzing:
- Claim history
- Hospital records
- Patient demographics
Deep learning algorithms also identify medical insurance fraud claims by analyzing fraudulent behaviors and health data from various resources, including claims history, hospital-related information, and patient attributes.
Research & Development
9. Drug discovery
Deep learning rapidly processes genomic, clinical, population datasets to:
- Identify viable drug combinations
- Predict drug interactions
This accelerates early stages of pharmaceutical R&D.
10. Genomics analysis
Deep learning models improve interpretability and enhance understanding of biological data. Their advanced data analysis capabilities support scientists in interpreting genetic variation and developing genome-based therapies. CNNs are widely used to extract features from fixed-size DNA sequence windows.
Real-life Example:
23andMe uses deep learning to analyze genetic data and provide insights into hereditary risks of conditions such as cancer and heart disease. An AI-driven platform helps individuals understand genetic variations that could impact their health.6 .
11. Mental health research
Researchers are working to enhance clinical practice in mental health by utilizing deep learning models. For example, there are ongoing academic studies about understanding the effects of mental illness and other disorders on the brain by using deep neural networks. Researchers suggest that training deep learning models in healthcare can yield better results in certain areas compared to standard machine learning models. For example, deep learning algorithms can learn to determine meaningful brain biomarkers.
Another study aims to build a cost-effective, digital, data-driven clinical decision-support system in mental health with machine learning capabilities.
12. Pandemics
Deep learning models have gained importance with the global COVID-19 outbreak. Researchers studied deep learning in healthcare applications for
- early detection of COVID-19
- analyzing of Chest X-ray (CXR) Chest CT images
- predicting intensive care unit admission
- helping to find potential patients who have high risk for Covid-19
- estimating need for mechanical ventilation
However, these tools did not reach wide-scale acceptance due to data issues and other adoption challenges.7
If you are ready to use deep learning in healthcare business, we have prepared a data-driven list of companies offering deep learning platforms. Also, feel free to check out our AI use cases in healthcare research.
You can also check the following data annotation services and tools list to select the option that best fits your business needs:
- Open-source data labeling platforms
- Data annotation services
- Medical data annotation tools
- Video annotation tools
The Newest Innovations in Deep Learning for Healthcare
Recent advancements in deep learning include Transformer-based models like OpenAI’s GPT-4 for medical language understanding and NVIDIA’s BioNeMo for biomedical research. These innovations enable faster analysis of medical texts, enhancing decision-making in healthcare.
Additionally, the integration of federated learning in platforms like Owkin ensures patient privacy while training deep learning in healthcare models across decentralized datasets. This approach is particularly impactful in collaborative research for rare diseases.
FAQ
Further reading
- Deep Learning in Finance Top 11 Use Cases
- Top 10 Use Cases of Generative AI in Education
- Top 50 Deep Learning Use Case & Case Studies
- Top 10 Applications of Deep Learning in Manufacturing
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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