Home > Media & Technology > Next Generation Technologies > AI and Machine Learning > AI in Predictive Toxicology Market
AI in Predictive Toxicology Market Size
- Report ID: GMI7363
- Published Date: Nov 2023
- Report Format: PDF
AI in Predictive Toxicology Market Size
AI in Predictive Toxicology Market size was valued at USD 281 million in 2022 and is estimated to register a CAGR of over 29.5% between 2023 and 2032. The increasing investments in pharmaceutical AI startups are driving the market growth. These funds enable the development and implementation of advanced technologies, such as Machine Learning (ML) and predictive modeling, to enhance toxicological assessments of chemical compounds
For instance, in December 2022, Quris Technologies Ltd., an Israeli pharmaceutical AI startup, gained an extra USD 9 million in seed funding, bringing the total raised amount to USD 37 million. The funding round was spearheaded by SoftBank Vision Fund 2, with contributions from current investors including GlenRock Capital, iAngels, Welltech Ventures, and Richter Group.
| Report Attribute | Details |
|---|---|
| Base Year: | 2022 |
| AI in Predictive Toxicology Market Size in 2022: | USD 281 Million |
| Forecast Period: | 2023 to 2032 |
| Forecast Period 2023 to 2032 CAGR: | 29.5% |
| 2032 Value Projection: | USD 3.67 Billion |
| Historical Data for: | 2018 – 2022 |
| No. of Pages: | 210 |
| Tables, Charts & Figures: | 347 |
| Segments covered: | Component, Technology, Toxicity Endpoints, and End User |
| Growth Drivers: |
|
| Pitfalls & Challenges: |
|
Advancements in AI technologies, particularly in ML and deep learning, play a pivotal role in propelling the AI in predictive toxicology market. These technologies enhance the capability to analyze complex data sets, recognize intricate patterns, and generate more accurate predictions regarding the toxicological properties of chemical compounds. The continuous refinement of AI algorithms and the integration of sophisticated computational techniques contribute to the development of robust & reliable models, making AI a key factor in advancing the field of predictive toxicology.
The quality and availability of data pose a significant barrier to the AI in predictive toxicology market growth. Inadequate or suboptimal datasets can compromise the training and validation of ML models, potentially leading to inaccurate predictions. Issues, such as data incompleteness, biases, or variability, can undermine the reliability of AI applications. Ensuring access to high-quality, diverse, and representative datasets is crucial for developing robust predictive models in toxicology, but acquiring such data can be a complex and resource-intensive task.
Frequently Asked Questions (FAQ) :
AI in Predictive Toxicology Market Scope
