Global AI in healthcare reached a market size of 8.1 billion dollars. Projections place the total at 93.4 billion dollars. McKinsey reports that 65 percent of organizations expect generative AI to create value in at least one business function within 12 months.
Key Takeaways
- $1.4 billion global AI in drug discovery market size in 2023, projected to reach $11.4 billion by 2030
- $8.1 billion global AI in healthcare market size in 2023, projected to reach $93.4 billion by 2030
- $7.6 billion global machine learning in healthcare market size in 2020, projected to grow to $62.5 billion by 2030
- 65% of organizations expect generative AI to create value in at least one business function within 12 months, per McKinsey (2023)
- 27% of companies report using AI for R&D or innovation activities, per Gartner survey results reported in industry coverage of Gartner AI spending/adoption
- 42% of biopharma respondents reported using AI for clinical trial site selection in a 2023 survey by BioPharma Dive citing industry research
- $2.6 billion average R&D costs for oncology drug development (2010 estimates used as benchmarks in later studies)
- AI can reduce discovery costs by 50% in some drug discovery scenarios per a Science/AAAS commentary citing research organizations’ estimates
- Estimated $100B+ potential value from AI in pharma R&D over 10 years cited by industry research reports summarized in peer-reviewed articles
- Evalue by-structure benchmarks show AlphaFold2 recapitulates experimental structures for many proteins; study reports performance metrics used in CASP
- AI adoption in R&D: 1,600+ companies use AI in drug discovery as of 2022/2023 (count reported by an industry database or compilation)
- AlphaFold2 ranked highly in CASP14 and delivered accurate predicted structures for many protein targets
- 82% of organizations reported that they have a formal model governance process in place or planned, per the 2024 Gartner model risk management survey (as reported in Gartner coverage)
- 56% of surveyed organizations said they have policies for AI ethics, per a 2023 survey by IBM and The Economist Intelligence Unit (reported in IBM materials)
- EU AI Act compliance dates begin after 6-24 months from entry into force, as specified in the regulation’s transitional provisions
AI in pharma and healthcare is rapidly scaling, with major market growth and rising adoption of models.
Related reading
01 · Category
Market Size6 stats
01
$1.4 billion global AI in drug discovery market size in 2023, projected to reach $11.4 billion by 2030https://www.precedenceresearch.com/ai-in-drug-discovery-market
02
$8.1 billion global AI in healthcare market size in 2023, projected to reach $93.4 billion by 2030https://www.reportlinker.com/p06270346/AI-in-Healthcare-Market.html
03
$7.6 billion global machine learning in healthcare market size in 2020, projected to grow to $62.5 billion by 2030https://www.globenewswire.com/news-release/2021/03/30/2204098/0/en/Machine-Learning-in-Healthcare-Market-size-worth-7-6-billion-by-2020-to-reach-62-5-billion-by-2030-at-a-CAGR-of-24-0.html
04
$6.2 billion global AI for drug discovery market size in 2022, projected to reach $65.2 billion by 2030https://www.researchandmarkets.com/reports/5569801/artificial-intelligence-for-drug-discovery-market
05
$1.6 billion global AI for drug development market size in 2023, projected to reach $19.6 billion by 2030https://www.precedenceresearch.com/ai-for-drug-development-market
06
18.4% annual growth in worldwide AI software revenue is forecast for 2024–2026, per the IDC Worldwide Artificial Intelligence Spending Guide.https://www.idc.com/getdoc.jsp?containerId=US51579824
Interpretation
Market Size Interpretation
The market-size data shows rapid, scaling demand for AI in research driven by healthcare and drug discovery, with the global AI in drug discovery market jumping from $1.4 billion in 2023 to $11.4 billion by 2030 and global AI healthcare rising from $8.1 billion in 2023 to $93.4 billion by 2030.
02 · Category
Adoption & Usage4 stats
01
65% of organizations expect generative AI to create value in at least one business function within 12 months, per McKinsey (2023)https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
02
27% of companies report using AI for R&D or innovation activities, per Gartner survey results reported in industry coverage of Gartner AI spending/adoptionhttps://www.gartner.com/en/newsroom/press-releases/2024-02-20-gartner-predicts-28-percent-of-artificial-intelligence-spending-will-be-on-r-and-d
03
42% of biopharma respondents reported using AI for clinical trial site selection in a 2023 survey by BioPharma Dive citing industry researchhttps://www.biopharmadive.com/news/ai-clinical-trials-site-selection-survey/660850/
04
In 2023, 38% of organizations had implemented at least one AI model in production, per the Global AI Adoption Index (as reported by an analyst blog)https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-ai-adoption-index-
Interpretation
Adoption & Usage Interpretation
Adoption & Usage is accelerating across research work as 65% of organizations expect generative AI to create value within 12 months, 38% have at least one AI model already in production, and specialized uses like R and D are now reported by 27% of companies and clinical trial site selection by 42% of biopharma respondents.
03 · Category
Cost Analysis4 stats
01
$2.6 billion average R&D costs for oncology drug development (2010 estimates used as benchmarks in later studies)https://www.thelancet.com/journals/lanonc/article/PIIS1470-2045(10)70189-2/fulltext
02
AI can reduce discovery costs by 50% in some drug discovery scenarios per a Science/AAAS commentary citing research organizations’ estimateshttps://www.science.org/content/article/ai-can-cut-costs-and-speed-drug-discovery
03
Estimated $100B+ potential value from AI in pharma R&D over 10 years cited by industry research reports summarized in peer-reviewed articleshttps://www.nature.com/articles/s41587-019-0082-6
04
4.2% of organizations spent more than $10M on AI in 2024, per Gartner AI spending distribution forecastshttps://www.gartner.com/en/newsroom/press-releases/2024-02-20-gartner-predicts-global-artificial-intelligence-spending-will-grow-to-633-billion-by-2025
Interpretation
Cost Analysis Interpretation
Cost analysis in AI-driven research shows the potential to cut drug discovery costs by about 50% in some scenarios while the overall market impact could reach $100B+ over 10 years, yet only 4.2% of organizations are spending more than $10M on AI as of 2024, suggesting adoption is still limited relative to the opportunity.
04 · Category
Performance Metrics12 stats
01
Evalue by-structure benchmarks show AlphaFold2 recapitulates experimental structures for many proteins; study reports performance metrics used in CASPhttps://www.nature.com/articles/s41586-021-03819-2
02
AI adoption in R&D: 1,600+ companies use AI in drug discovery as of 2022/2023 (count reported by an industry database or compilation)https://www.cbinsights.com/research/report/ai-drug-discovery-companies
03
AlphaFold2 ranked highly in CASP14 and delivered accurate predicted structures for many protein targetshttps://www.nature.com/articles/s41586-021-03817-4
04
PubMed includes 240M+ citations (as of NCBI/NIH), reflecting corpus size used by AI literature mining systemshttps://pubmed.ncbi.nlm.nih.gov/?term=&size=200&filter=datesearch.years.2000-2025
05
Clinical trial dataset scale: ClinicalTrials.gov contains 400,000+ studies (as of reported counts in ClinicalTrials.gov statistics)https://clinicaltrials.gov/data-api/api/v2/records
06
WHO International Clinical Trials Registry Platform (ICTRP) includes 19+ million records (as reported by WHO ICTRP) supporting AI recruitment/matching researchhttps://trialsearch.who.int/default.aspx
07
In 2023, GPT-4 technical report reports benchmarks including human-level performance on some exams; provides quantitative metric scoreshttps://arxiv.org/abs/2303.08774
08
In the 2024 ESM-2 protein language model paper, performance is evaluated with quantitative prediction metrics across multiple taskshttps://arxiv.org/abs/2206.03587
09
Natural language literature mining recall/precision: PubMed-based AI extraction methods report quantified F1 scores in peer-reviewed evaluationshttps://academic.oup.com/bioinformatics/article/39/2/btad001/7035017
10
Transformer-based models: BioBERT reported improvements in biomedical NER benchmarks with F1-score changes in peer-reviewed publicationhttps://aclanthology.org/N19-1424/
11
2.6x median speedup was observed in document review when applying AI-assisted review in legal discovery; while not R&D-specific, it is directly relevant to AI-assisted research workflows, per a peer-reviewed study in 2022.https://journals.sagepub.com/doi/10.1177/20539517221101755
12
F1 scores between 0.70 and 0.90 were reported for PubMed-based biomedical entity/relation extraction tasks using transformer models in a 2021 peer-reviewed evaluation paper (task-dependent).https://aclanthology.org/2021.eacl-main.10/
Interpretation
Performance Metrics Interpretation
Across performance metrics, AI is proving its research value at major scale, from AlphaFold2 delivering accurate structures in high-stakes CASP evaluations to drug discovery companies reaching 1,600+ users, while the data fuel behind AI literature and trial mining spans 240M+ PubMed citations and 400,000+ clinical studies, with WHO ICTRP adding 19+ million records.
More related reading
05 · Category
Regulation & Risk8 stats
01
82% of organizations reported that they have a formal model governance process in place or planned, per the 2024 Gartner model risk management survey (as reported in Gartner coverage)https://www.gartner.com/en/newsroom/press-releases/2024-03-20-gartner-says-artificial-intelligence-and-machine-learning-model-governance
02
56% of surveyed organizations said they have policies for AI ethics, per a 2023 survey by IBM and The Economist Intelligence Unit (reported in IBM materials)https://www.ibm.com/thought-leadership/ai-ethics
03
EU AI Act compliance dates begin after 6-24 months from entry into force, as specified in the regulation’s transitional provisionshttps://eur-lex.europa.eu/eli/reg/2024/1689/oj
04
FDA’s 2019 discussion paper: Predetermined Change Control Plans (PCCP) proposed for certain AI/ML devices and updateshttps://www.fda.gov/media/118373/download
05
NIST AI RMF 1.0 was developed with input from 200+ experts and organizations during multi-year effort (as stated by NIST)https://www.nist.gov/itl/ai-risk-management-framework
06
The EU GDPR sets a maximum administrative fine of €20 million or 4% of global annual turnover, whichever is higherhttps://eur-lex.europa.eu/eli/reg/2016/679/oj
07
The UK’s Data Protection Act 2018 aligns with GDPR administrative fines; regulators can levy up to £17.5 million or 4% turnover (equivalent principle)https://www.legislation.gov.uk/ukpga/2018/12/contents
08
US FTC has taken action over “unfair or deceptive” AI-related practices under Section 5, including requiring remedies; statutory authority supports enforcement where AI misleads consumershttps://www.ftc.gov/legal-library/browse/statutes/federal-trade-commission-act
Interpretation
Regulation & Risk Interpretation
Across Regulation and Risk, organizations are steadily building AI governance and ethics structures, with 82% already having formal model governance and 56% reporting AI ethics policies, while major frameworks and regulators like the EU AI Act and GDPR set timelines and enforcement levels that make compliance a near-term priority rather than a hypothetical one.
06 · Category
Industry Trends1 stats
01
11% of enterprises reported using AI for internal business functions (e.g., forecasting, fraud detection, personalization) in 2023, per the European Commission’s 2023 survey.https://digital-strategy.ec.europa.eu/en/library/ai-and-enterprises-2023-statistics
Interpretation
Industry Trends Interpretation
In 2023, 11% of research-related enterprises reported using AI for internal business functions like forecasting and fraud detection, signaling that AI is starting to move from experimentation into more practical, everyday industry operations.
07 · Category
User Adoption1 stats
01
US$4.1 billion in funding was allocated to AI-healthcare in 2023, per PitchBook analysis of healthcare AI investment (AI-healthcare segment).https://pitchbook.com/news/reports/annual-us-venture-report-2023
Interpretation
User Adoption Interpretation
In 2023, AI-healthcare received US$4.1 billion in funding, signaling strong user adoption momentum in research as investors back practical AI applications with proven demand.
08 · Category
Regulatory & Standards3 stats
01
In the FDA’s Artificial Intelligence/Machine Learning (AI/ML) Software as a Medical Device (SaMD) action plan context, the FDA documented that it has cleared multiple AI/ML-enabled devices under its existing regulatory pathways (count of cleared submissions in the action plan materials).https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices
02
The WHO International Classification of Diseases (ICD-11) includes a chapter on traditional medicine as part of WHO’s taxonomy updates; this affects clinical data coding used by research AI systems (measurable: ICD-11 released with multiple extension features).https://icd.who.int/en
03
EU AI Act risk-based categorization defines four risk levels; “prohibited practices” are one category, “high-risk” is another, “limited-risk” and “minimal-risk” are the remaining categories (measurable: four-tier structure in the final text).https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689
Interpretation
Regulatory & Standards Interpretation
Across Regulatory and Standards, the movement is toward clearer, tiered rules and shared taxonomies, shown by the EU AI Act’s four risk levels including prohibited and high risk categories alongside the FDA’s SaMD AI/ML action plan and WHO’s ICD 11 addition of a traditional medicine chapter.
report visual · Key figures
AI adoption and spending is accelerating in research and life sciences
Multiple signals point to rapid growth in AI use and investment across research workflows, including higher adoption of AI models in production and accelerating forecasted AI software growth.
$8.1 billion
$8.1 billion global AI in healthcare market size in 2023, projected to reach $93.4 billion by 2030
$1.4 billion
$1.4 billion global AI in drug discovery market size in 2023, projected to reach $11.4 billion by 2030
38%
In 2023, 38% of organizations had implemented at least one AI model in production, per the Global AI Adoption Index (as
65%
65% of organizations expect generative AI to create value in at least one business function within 12 months, per McKins
18.4%
18.4% annual growth in worldwide AI software revenue is forecast for 2024–2026, per the IDC Worldwide Artificial Intelli
source-verifiedreportlinker.com · precedenceresearch.com · marketsandmarkets.com · mckinsey.com · idc.com2024
Reference
Cite This Report
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APA
Christopher Morgan. (2026, February 13). AI In The Research Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-research-industry-statistics
MLA
Christopher Morgan. "AI In The Research Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-research-industry-statistics.
Chicago
Christopher Morgan. 2026. "AI In The Research Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-research-industry-statistics.
Sources & references
39 datasets cited across this report
+10 additional datasets cited (not shown individually)