2026 Global: Artificial Intelligence (Ai) In Life Sciences-Competitive Review (2032) report
Description
The 2026 Global: Artificial Intelligence (Ai) In Life Sciences-Competitive Review (2031) report features the global market size and projected growth/decline data for the period 2021 through 2032. The report primarily provides an examination of the business strategies for the ten largest global companies in the market and how their strategies differ.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for artificial intelligence (ai) in life sciences by geography and historical trend. The scope of the report extends to sizing of the artificial intelligence (ai) in life sciences market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
PathAI, Tempus, Insitro, Recursion Pharmaceuticals, and Generate Biomedicines lead the top ten AI companies transforming life sciences through diagnostics, precision medicine, and drug discovery. PathAI pioneers digital pathology with machine learning for tissue analysis, reducing errors in cancer diagnostics and aiding pharmaceutical biomarker discovery. Tempus leverages vast molecular and clinical data libraries to power AI models for personalized oncology treatments, integrating electronic health records for pattern recognition across millions of cases. Insitro applies generative AI and machine learning to decode biological data, accelerating drug target identification via autoencoder technologies and partnerships like Alphabet affiliations. Recursion Pharmaceuticals uses AI to analyze biological experiments, holding patents in machine learning for cellular imaging and phenotypic screening to expedite therapeutic development. Generate Biomedicines employs machine learning platforms to design novel protein therapeutics, simulating protein structures for diseases and partnering with life sciences firms for FDA-validated projects.
Guardant Health, Gritstone Bio, Isomorphic Labs, and EvolutionaryScale excel in AI-driven oncology and protein engineering for life sciences innovation. Guardant Health ranks high with 26 AI patents since 2020, utilizing machine learning for liquid biopsy analysis in cancer detection and monitoring. Gritstone Bio tops patent filings at 33, focusing AI on immune response prediction for cancer immunotherapies, demonstrating small biotechs' IP prowess in predictive modeling. Isomorphic Labs, linked to Google DeepMind, advances protein therapeutics with AlphaFold3 collaborations alongside Novartis, targeting immunology and infectious diseases through AI-generated drug candidates. EvolutionaryScale develops ESM3 models trained on billions of proteins to generate novel structures, enabling evolutionary simulations for therapeutic proteins in research applications.
BigHat Biosciences and BPGbio round out the list, enhancing antibody design and causal AI for clinical pipelines. BigHat Biosciences integrates machine learning with synthetic biology in its Milliner platform to optimize antibodies for cancer and inflammation, iterating on affinity and stability. BPGbio harnesses causal AI on supercomputers like Frontier for novel drug targets in oncology and neurology, bolstered by acquisitions and partnerships with AstraZeneca. These companies collectively drive AI adoption in life sciences, from genomic analytics to generative biology, fostering faster, precise advancements amid 2025's patent surge and clinical integrations.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for artificial intelligence (ai) in life sciences by geography and historical trend. The scope of the report extends to sizing of the artificial intelligence (ai) in life sciences market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
PathAI, Tempus, Insitro, Recursion Pharmaceuticals, and Generate Biomedicines lead the top ten AI companies transforming life sciences through diagnostics, precision medicine, and drug discovery. PathAI pioneers digital pathology with machine learning for tissue analysis, reducing errors in cancer diagnostics and aiding pharmaceutical biomarker discovery. Tempus leverages vast molecular and clinical data libraries to power AI models for personalized oncology treatments, integrating electronic health records for pattern recognition across millions of cases. Insitro applies generative AI and machine learning to decode biological data, accelerating drug target identification via autoencoder technologies and partnerships like Alphabet affiliations. Recursion Pharmaceuticals uses AI to analyze biological experiments, holding patents in machine learning for cellular imaging and phenotypic screening to expedite therapeutic development. Generate Biomedicines employs machine learning platforms to design novel protein therapeutics, simulating protein structures for diseases and partnering with life sciences firms for FDA-validated projects.
Guardant Health, Gritstone Bio, Isomorphic Labs, and EvolutionaryScale excel in AI-driven oncology and protein engineering for life sciences innovation. Guardant Health ranks high with 26 AI patents since 2020, utilizing machine learning for liquid biopsy analysis in cancer detection and monitoring. Gritstone Bio tops patent filings at 33, focusing AI on immune response prediction for cancer immunotherapies, demonstrating small biotechs' IP prowess in predictive modeling. Isomorphic Labs, linked to Google DeepMind, advances protein therapeutics with AlphaFold3 collaborations alongside Novartis, targeting immunology and infectious diseases through AI-generated drug candidates. EvolutionaryScale develops ESM3 models trained on billions of proteins to generate novel structures, enabling evolutionary simulations for therapeutic proteins in research applications.
BigHat Biosciences and BPGbio round out the list, enhancing antibody design and causal AI for clinical pipelines. BigHat Biosciences integrates machine learning with synthetic biology in its Milliner platform to optimize antibodies for cancer and inflammation, iterating on affinity and stability. BPGbio harnesses causal AI on supercomputers like Frontier for novel drug targets in oncology and neurology, bolstered by acquisitions and partnerships with AstraZeneca. These companies collectively drive AI adoption in life sciences, from genomic analytics to generative biology, fostering faster, precise advancements amid 2025's patent surge and clinical integrations.
Table of Contents
32 Pages
- 1.0 Scope of Report and Methodology
- 2.0 Market SWOT Analysis and Players
- 2.1 Market Definition
- 2.2 Market Segments
- 2.3 Market Strengths
- 2.4 Market Weaknesses
- 2.5 Market Threats
- 2.6 Market Opportunities
- 2.7 Major Players
- 3.0 Competitive Analysis
- 3.1 Market Player 1
- 3.2 Market Player 2
- 3.3 Market Player 3
- 3.4 Market Player 4
- 3.5 Market Player 5
- 3.6 Market Player 6
- 3.7 Market Player 7
- 3.8 Market Player 8
- 3.9 Market Player 9
- 3.10 Market Player 10
- 4.0 Comparative Business Strategies
- 4.1 Comparative Business Strategies of Player 1 and 2
- 4.2 Comparative Business Strategies of Player 1 and 3
- 4.3 Comparative Business Strategies of Player 1 and 4
- 4.4 Comparative Business Strategies of Player 2 and 3
- 4.5 Comparative Business Strategies of Player 2 and 4
- 4.6 Comparative Business Strategies of Player 3 and 4
- 5.0 Appendix
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