Key Industries & Applications in the Global Deep Learning Market

Deep Learning Market - Inkwood Research

Over the years, deep learning has emerged as a transformative technology, revolutionizing the way various industries operate. Accordingly, the domain has become instrumental in the field of data analysis, predictive modeling, and process optimization. As per Inkwood Research, the global deep learning market is anticipated to grow with a CAGR of 39.67% during the forecast period 2023-2032.

In this blog, we will explore how major industries are leveraging the potential of deep learning in order to drive innovation and efficiency in their operations. We will also highlight real-world examples of successful deep learning applications in each of these industries.

1. Healthcare

Healthcare organizations of all specialties and types are becoming increasingly inclined toward how artificial intelligence (AI) can facilitate better patient care, while improving efficiencies and reducing costs. In this regard, the healthcare industry has been at the forefront of adopting deep learning for a wide range of applications, such as –

  • Medical Imaging: Deep learning algorithms have significantly improved the accuracy of medical image analysis. For instance, Aidoc (Israel), a leading provider of AI-powered radiology solutions, aims to improve the efficacy as well as accuracy of radiology diagnoses. The company’s platform utilizes deep learning algorithms in order to analyze medical images and aid radiologists in prioritizing and detecting critical findings.
  • Drug Discovery: Pharmaceutical companies are using deep learning to expedite drug discovery processes. Insilico Medicine (United States), for example, employs deep learning models to predict potential drug candidates, reducing the time and cost associated with developing new medications. The company’s early bet on deep learning is yielding significant results – a drug candidate discovered via its AI platform is now entering Phase II clinical trials for the treatment of idiopathic pulmonary fibrosis.
  • Disease Diagnosis: Deep learning is aiding in disease diagnosis through predictive modeling. PathAI (United States), for instance, utilizes deep learning-based AI solutions to assist pathologists in the detection, diagnosis, and prognosis of several cancer subtypes, thus improving the accuracy of diagnoses and patient outcomes.

2. Finance & Banking Services

The success of deep learning as a data processing technique has piqued the interest of the financial research community. Moreover, with the proliferation of Fintech over recent years, the use of deep learning in the finance & banking services industry has become highly prevalent across the following applications –

  • Fraud Detection: Banks and financial institutions deploy deep learning models to detect fraudulent activities in real time. Companies like Feedzai (Portugal) use deep learning algorithms to analyze transaction data and identify unusual patterns indicative of fraud. In July 2023, the company announced the launch of Railgun, a next-generation fraud detection engine, featuring advanced AI to secure millions from the surge in financial crime.
  • Algorithmic Trading: Hedge funds and trading firms leverage deep learning for algorithmic trading. Investors are utilizing deep learning models to evaluate and anticipate stock and foreign exchange markets, given the advantage of artificial intelligence.
  • Credit Scoring: Deep learning is transforming the credit scoring landscape. LenddoEFL (Singapore), for instance, creates, collects, and analyzes information from consent-based alternative data sources for an accurate understanding of creditworthiness. The company’s unique credit decisioning tools draw from large, diverse, and unstructured data sources through deep learning, artificial intelligence, and advanced modeling techniques.

3. Manufacturing

Here’s how the manufacturing industry benefits from deep learning in optimizing production processes and quality control –

  • Predictive Maintenance: Manufacturers use deep learning to predict equipment failures and schedule maintenance proactively. General Electric’s (United States) Predix platform employs deep learning to support innovative IoT solutions to help reduce downtime and maintenance costs.
  • Quality Control: Deep learning-based image recognition systems inspect products for defects on production lines. Real-time deep learning approaches are essential for automated industrial processes in product manufacturing, where vision-based systems effectively control the fabrication quality on the basis of specific structural designs.
  • Supply Chain Optimization: According to industry sources, deep learning models have the potential to generate between $1-2 trillion annually in supply chain management. In this regard, deep learning is used in the manufacturing sector to optimize supply chain operations by predicting demand patterns, enhancing inventory levels, and improving logistics planning.


Deep learning’s ability to process vast amounts of data and recognize complex patterns is transforming the way industries operate, making processes more efficient and enhancing customer experiences. As businesses strive to remain competitive in an increasingly data-driven world, leveraging deep learning and staying updated on its latest developments will be crucial for the overall growth of the global deep learning market.

By Shruti Vanjare


What are the key components of a deep learning system?

A deep learning system typically comprises input data, a deep neural network architecture, loss functions, optimization algorithms, and labeled training data.

How does deep learning differ from traditional machine learning?

Deep learning, unlike traditional machine learning, eliminates the need for manual feature engineering by allowing models to learn and extract features automatically from data.