According to a research report "Explainable AI Market by Offering (Solutions & Services), Software Type (Standalone Software, Integrated Software, Automated Reporting Tools, Interactive Model Visualization), Methods, Vertical and Region - Global Forecast to 2028" published by MarketsandMarkets, the global explainable AI market size is to grow from USD 6.2 billion in 2023 to USD 16.2 billion by 2028, at a Compound Annual Growth Rate (CAGR) of 20.9% during the forecast period. Explainable AI is at the intersection of AI, machine learning, ethics, and psychology. Collaboration across these fields is driving innovation in making AI systems more interpretable and user-friendly.
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As per verticals, the healthcare & life sciences segment is to grow at the highest CAGR during the forecast period.
The healthcare & life sciences vertical is expected to grow at the highest CAGR during the forecast period. The healthcare and life sciences industry stands at the forefront of leveraging Explainable AI to enhance decision-making processes and improve patient outcomes. In this vertical, Explainable AI technologies are employed to make the predictions and recommendations generated by AI models more transparent and interpretable for clinicians, researchers, and regulatory bodies. As the application of AI in healthcare becomes increasingly widespread, ensuring that AI-driven diagnostics, treatment recommendations, and drug discovery processes are explainable is crucial to build trust and mitigate risks.
By Solutions by type, Software toolkits and frameworks Segment to grow at the largest market size during the forecast period.
Software toolkits and frameworks for Explainable AI are more developer-centric solutions. They offer libraries, APIs, and pre-built algorithms that data scientists and machine learning engineers can integrate into their existing machine learning workflows. These toolkits provide flexibility and customization options for those who want to implement transparency and interpretability in AI models. Toolkits provide a variety of algorithms for explaining and interpreting AI model predictions, such as feature importance, partial dependence plots, and saliency maps.
By Methods, the Model-Agnostic Methods segment is to grow at the highest CAGR during the forecast period.
Model-agnostic methods are techniques that can be applied to any machine learning model, irrespective of the underlying architecture. They are often used when a black-box model, like a deep neural network, is in place, and the goal is to provide explanations without altering the model itself. Companies utilize these methods to make AI models more interpretable and to build trust with users and stakeholders.
Asia Pacific to account to grow at the highest CAGR during the forecast period.
By region, Asia Pacific countries, especially China, South Korea, and Japan, are at the forefront of technological innovation. They are quick to adopt and adapt to emerging AI technologies, including XAI, in various industries. The APAC region is home to a significant portion of the global population, with diverse industries ranging from manufacturing to healthcare, finance, and e-commerce. This diversity creates a wide array of use cases for XAI. Governments are actively promoting AI research and development. They are also introducing regulations that require AI systems to be transparent and explainable, contributing to the growth of the explainable AI market.
Some major players in the explainable AI market include Microsoft (US), IBM (US), Google (US), Salesforce (US),Intel Corporation(US), NVIDIA(US), SAS Institute(US), Alteryx(US), AWS(US), Equifax(US), FICO(US), Temenos(Switzerland),Mphasis(India), C3.AI(US), H2O.ai(US), Fiddler(US), Zest AI(US), Seldon(London), Squirro(Switzerland), Kyndi(US), DataRobot(US),Databricks(US), Tredence(US), DarwinAI(Canada), Tensor AI solutions(Germany), . EXPAI(Spain), Abzu(Denmark), Arthur(US), and Intellico(Italy.
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