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12 Deep Learning Use Cases / Applications in Healthcare [2024]

The computing capability of deep learning models has enabled fast, accurate and efficient operations in healthcare. Deep learning networks are transforming patient care and they have a fundamental role for health systems in clinical practice. Computer vision, natural language processing, reinforcement learning are the most commonly used deep learning techniques in healthcare.

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. Medical imaging

Image recognition and object detection are used in Magnetic Resonance (MR) and Computed tomography (CT) processes for image segmentation, disease detection & prediction. Deep learning models can make effective interpretations by a combination of aspects of imaging data, for example, tissue size, volume, and shape. These models can flag important areas in images. For example, deep learning algorithms are used for diabetic retinopathy detection, early detection of Alzheimer and ultrasound detection of breast nodules. Thanks to new advances in deep learning, most pathology and radiology images can be investigated in the future.

Deep learning algorithms simplify complex data analysis, so abnormalities are determined and prioritized more precisely. The insights that convolutional neural networks (CNNs) provide, help medical professionals to notice the health issues of their patients on time and more accurately. For example, CNNs identified melanoma disease in dermatology images with more than 10% accuracy than experts according to a study in 2018.

You can also read our article on Computer vision in the healthcare sector and companies in AI-powered medical imaging.

2. Healthcare data analytics

Deep learning models can analyze electronic health records (EHR) that contain structured and unstructured data, including clinical notes, laboratory test results, diagnosis, and medications at exceptional speeds with the most possible accuracy.

Also, smartphones and wearable devices provide useful information about lifestyle. They have the potential to transform data by using mobile apps to monitor medical risk factors for deep learning models. In 2019, Current Health’s AI wearable device became one of the first AI medical monitoring wearables approved by Food and Drug Administration (FDA) for use at home. This device can measure the pulse, respiration, oxygen saturation, temperature, and mobility of patients.

Feel free to read our examples on Healthcare Analytics for more.

3. Mental health chatbots

The use of AI-based mental health apps (including chatbots) such as Happify, Moodkit, Woebot, Wysa is increasing. Some of these chatbots can leverage deep learning models for more realistic conversations with patients. A study by Stanford University shows that an intelligent conversational agent can significantly decrease depression and anxiety symptoms in students and it is an efficient and engaging way to deliver mental health support.

4. Personalized medical treatments

Deep learning solutions allow healthcare organizations to deliver personalized patient care by analyzing patients’ medical history, symptoms, and tests. Natural language processing (NLP) provides insights from free-text medical information for most relevant medical treatments.

5. Prescription audit

Deep learning models can audit prescriptions vs patient health records to identify and correct possible diagnostic errors or errors in prescription.

6. Responding to patient queries

Deep learning-based chatbots support healthcare professionals or patients themselves to identify patterns in patient symptoms.

Health Insurance

7. Underwriting

Deep learning models help insurance companies to make offers to their customers by powerful predictive analytics. For more, check our article on how AI is used to improve underwriting processes.

8. Fraud detection

Also, deep learning algorithms identify medical insurance fraud claims by analyzing fraudulent behaviors and health data from different resources such as claims history, hospital-related information, and patient attributes.

Research & Development

9. Drug discovery

Contributions of deep learning models in the discovery and interaction prediction of drugs have been growing with new technological advances. Deep learning algorithms are able to identify viable drug combinations by processing genomic, clinical, and population data rapidly. Researchers in the pharmaceutical industry take advantage of deep learning toolkits to focus on patterns in these large data sets.

10. Genomics analysis

Deep learning models increase interpretability and provide a better understanding of biological data. Complex data analyzing capabilities of deep learning models support scientists while they study the interpretation of genetic variation and genome-based therapeutic development. CNNs are commonly used and they enable scientists to get attributes from fixed-size DNA sequence windows.

11. Mental health research

Researchers are trying to improve clinical practice in mental health by using 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 say that trained deep learning models can provide better results in some 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 and digital data-driven and clinical decision support system in mental health with machine learning capabilities.

12. Covid-19

Usage of deep learning models has gained importance with the global COVID-19 outbreak. Researchers have started to study deep learning 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

If you are ready to use deep learning in your business, we 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:

Further reading

If you need help in choosing among deep learning vendors who can help you get started, let us know:

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This article was drafted by former AIMultiple industry analyst Ayşegül Takımoğlu.

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related 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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