From Algorithms to Startups: A Systematic Literature Review of Artificial Intelligence’s Role in Shaping Healthcare Entrepreneurship

The convergence of artificial intelligence (AI) and healthcare is creating new opportunities for entrepreneurship, innovation, and value creation. However, existing research remains fragmented across healthcare, technology, entrepreneurship, and related disciplines, limiting a consolidated understanding of how AI shapes healthcare entrepreneurship. This study conducts a systematic literature review (SLR) to examine the role of AI in healthcare entrepreneurship, the challenges associated with its adoption, and its broader impacts on the sector. Following PRISMA 2020 guidelines, 484 records were retrieved from the Web of Science and Scopus databases, of which 19 peer-reviewed journal articles published between 2020 and 2025 met the inclusion criteria. The findings show that AI supports innovation in diagnostics, personalised care, operational efficiency, predictive analytics, and venture scalability, while enabling startups to develop new value propositions and healthcare delivery models. However, adoption remains constrained by regulatory uncertainty, data governance, ethical concerns, institutional resistance, and limited technical literacy. The review develops a conceptual framework linking AI capabilities across three entrepreneurial stages: ideation, venture development, and implementation. The findings highlight that sustainable AI-driven healthcare entrepreneurship requires alignment between technological innovation, clinical needs, regulatory requirements, and stakeholder trust. Future research should empirically validate the proposed framework through longitudinal and cross-cultural studies, examine AI entrepreneurship in diverse healthcare settings, and investigate the effectiveness of education and accelerator programmes in strengthening entrepreneurial capabilities and responsible AI adoption.

Artificial Intelligence, Digital Health, Healthcare Entrepreneurship, Systematic Literature Review

1. Achuthan, K., Ramanathan, S., & Raman, R. (2025). Exploring metaverse technologies in entrepreneurship through machine learning based topic modeling. IEEE Access. [Google Scholar] [Crossref]

2. https://doi.org/10.1109/ACCESS.2025.3567622 [Google Scholar] [Crossref]

3. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa [Google Scholar] [Crossref]

4. Chen, M., Cui, D., Haick, H., & Tang, N. (2024). Artificial intelligence-based medical sensors for healthcare system. Advanced Sensor Research, 3(3), 2300009. https://doi.org/10.1002/adsr.202300009 [Google Scholar] [Crossref]

5. Dijmarescu, I. (2021). Some characteristics of entrepreneurship in medicine. The digitalization of the medical system in Romania. Romanian Journal of Pediatrics, 70(2), 99-102. [Google Scholar] [Crossref]

6. https://doi.org/10.37897/RJP.2021.2.2 [Google Scholar] [Crossref]

7. Fritzsche, H., Barbazzeni, B., Mahmeen, M., Haider, S., & Friebe, M. (2021). A structured pathway toward disruption: A novel healthtech innovation design curriculum with entrepreneurship in mind. Frontiers in Public Health, 9, 715768. https://doi.org/10.3389/fpubh.2021.715768 [Google Scholar] [Crossref]

8. Gennatas, E. D., & Chen, J. H. (2021). Artificial intelligence in medicine: Past, present, and future. In Artificial Intelligence in Medicine (pp. 3-18). Academic Press. https://doi.org/10.1016/B978-0-12-821259-2.00001-6 [Google Scholar] [Crossref]

9. Giones, F., & Brem, A. (2017). Digital technology entrepreneurship: A definition and research agenda. Technology Innovation Management Review, 7(5), 44-51. [Google Scholar] [Crossref]

10. Glover, W. J., Crocker, A., & Brush, C. G. (2024). Healthcare entrepreneurship: An integrative framework for future research. Journal of Business Venturing Insights, 22, e00476. [Google Scholar] [Crossref]

11. https://doi.org/10.1016/j.jbvi.2024.e00476 [Google Scholar] [Crossref]

12. Grüning, Maximilian; Metzler, Dennis Renee; and Trenz, Manuel. (2023). Towards Value Creation with Artificial Intelligence in Healthcare: A Qualitative Study on User Requirements. PACIS 2023 Proceedings. 119. [Google Scholar] [Crossref]

13. https://aisel.aisnet.org/pacis2023/119 [Google Scholar] [Crossref]

14. Gusenbauer, M., & Haddaway, N. R. (2020). Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Research Synthesis Methods, 11(2), 181-217. https://doi.org/10.1002/jrsm.1378 [Google Scholar] [Crossref]

15. Hayder, M. A. Ghanimi, Painoli, G. K., Contractor, D., Sowjanya, G. N., Vani Lavanya, A., & Poon, K. (2024). Analyzing the impact of scientific and technological innovations on financial performance: A PCA-based study of an AI healthcare startup. Journal of Machine and Computing, 4(4), 1117-1125. https://doi.org/10.53759/7669/jmc202404103 [Google Scholar] [Crossref]

16. Joerin, A., Rauws, M., Fulmer, R., & Black, V. (2020). Ethical artificial intelligence for digital health organizations. Cureus, 12(3), e7202. https://doi.org/10.7759/cureus.7202 [Google Scholar] [Crossref]

17. Jovy-Klein, F., Stead, S., Salge, T. O., Sander, J., Diehl, A., & Antons, D. (2024). Forecasting the future of smart hospitals: Findings from a real-time delphi study. BMC Health Services Research, 24(1), 1421. https://doi.org/10.1186/s12913-024-11895-z [Google Scholar] [Crossref]

18. Kais, E. C. F., Ghafouri, A., & Al Shamsi, M. (2021). A Review of the Impact of Artificial Intelligence on the Healthcare Industry: A United Arab Emirates Perspective. http://hdl.handle.net/11073/21507 [Google Scholar] [Crossref]

19. Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele University, 33(2004), 1-26. [Google Scholar] [Crossref]

20. Korzynski, P., Mazurek, G., Krzypkowska, P., & Kurasinski, A. (2023). Artificial intelligence prompt engineering as a new digital competence: Analysis of generative AI technologies such as ChatGPT. Entrepreneurial Business and Economics Review, 11(3), 25-37. [Google Scholar] [Crossref]

21. https://doi.org/10.15678/EBER.2023.110302 [Google Scholar] [Crossref]

22. Kulkov, I., Kulkova, J., Leone, D., Rohrbeck, R., & Menvielle, L. (2023a). Stand-alone or run together: Artificial intelligence as an enabler for other technologies. International Journal of Entrepreneurial Behavior & Research, 30(8), 2082-2105. https://doi.org/10.1108/IJEBR-02-2023-0169 [Google Scholar] [Crossref]

23. Kulkov, I., Ivanova-Gongne, M., Bertello, A., Makkonen, H., Kulkova, J., Rohrbeck, R., & Ferraris, A. (2023b). Technology entrepreneurship in healthcare: Challenges and opportunities for value creation. Journal of Innovation & Knowledge, 8(2), 100365. https://doi.org/10.1016/j.jik.2023.100365 [Google Scholar] [Crossref]

24. Kumar, S., Lim, W. M., Sivarajah, U., & Kaur, J. (2023). Artificial intelligence and blockchain integration in business: Trends from a bibliometric-content analysis. Information Systems Frontiers, 25(2), 871-896. https://doi.org/10.1007/s10796-022-10279-0 [Google Scholar] [Crossref]

25. Liu, S. Q., Vakeel, K. A., Smith, N. A., Alavipour, R. S., Wei, C., & Wirtz, J. (2024). AI concierge in the customer journey: What is it and how can it add value to the customer? Journal of Service Management, 35(6), 136-158. https://doi.org/10.1108/JOSM-12-2023-0523 [Google Scholar] [Crossref]

26. Mavrogiorgou, A., Kiourtis, A., Kleftakis, S., Mavrogiorgos, K., Zafeiropoulos, N., & Kyriazis, D. (2022). A catalogue of machine learning algorithms for healthcare risk predictions. Sensors, 22(22), 8615. https://doi.org/10.3390/s22228615 [Google Scholar] [Crossref]

27. Olaye, I. M., & Seixas, A. A. (2023). The gap between AI and bedside: Participatory workshop on the barriers to the integration, translation, and adoption of digital health care and AI startup technology into clinical practice. Journal of Medical Internet Research, 25, e32962. https://doi.org/10.2196/32962 [Google Scholar] [Crossref]

28. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. PLOS Medicine, 18(3), e1003583. https://doi.org/10.1136/bmj.n71 [Google Scholar] [Crossref]

29. Phillips, A. (2021). Artificial intelligence-enabled healthcare delivery and digital epidemiological surveillance in the remote treatment of patients during the COVID-19 pandemic. American Journal of Medical Research, 8(1), 40-49. [Google Scholar] [Crossref]

30. Roppelt, J. S., Jenkins, A., Kanbach, D. K., Kraus, S., & Jones, P. (2025). Effective adoption of artificial intelligence in healthcare: A multiple case study. Journal of Decision Systems, 34(1), 2458883. https://doi.org/10.1080/12460125.2025.2458883 [Google Scholar] [Crossref]

31. Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Hachette UK. [Google Scholar] [Crossref]

32. Vandersluis, R., & Savulescu, J. (2024). The selective deployment of AI in healthcare: An ethical algorithm for algorithms. Bioethics, 38(5), 391-400. https://doi.org/10.1111/bioe.13281 [Google Scholar] [Crossref]

33. Wang, Z., Chen, X., Wu, Y., Jiang, L., Lin, S., & Qiu, G. (2025). A robust and interpretable ensemble machine learning model for predicting healthcare insurance fraud. Scientific Reports, 15(1), 218. https://doi.org/10.1038/s41598-024-82062-x [Google Scholar] [Crossref]

34. Young, A. S. (2022). AI in healthcare startups and special challenges. Intelligence-Based Medicine, 6, 100050. https://doi.org/10.1016/j.ibmed.2022.100050 [Google Scholar] [Crossref]

35. Zahlan, A., Ranjan, R. P., & Hayes, D. (2023). Artificial intelligence innovation in healthcare: Literature review, exploratory analysis, and future research. Technology in society, 74, 102321. [Google Scholar] [Crossref]

36. https://doi.org/10.1016/j.techsoc.2023.102321 [Google Scholar] [Crossref]

37. Zhu, J., & Liu, W. (2020). A tale of two databases: The use of Web of Science and Scopus in academic papers. Scientometrics, 123(1), 321-335. https://doi.org/10.1007/s11192-020-03387-8 [Google Scholar] [Crossref]

38. Zou, S., Chu, C., Shen, N., & Ren, J. (2023). Healthcare cost prediction based on hybrid machine learning algorithms. Mathematics, 11(23), 4778. https://doi.org/10.3390/math11234778 [Google Scholar] [Crossref]

• Entrepreneurship and Start-Up Ecosystem Vis-À-Vis Government Start-Up Scheme in Manipur: A Critical Review and a Way Forward
• Building Resilience: How Malaysian Women Entrepreneurs Use Strengths and Resources to Navigate Challenges
• Branding in Digital Entrepreneurship: Evaluating Branding Criteria in IT Entrepreneurship
• Bridging the Intention–Action Gap through Entrepreneurial Optimism: A Conceptual Exploration
• Entrepreneurial Orientation of Undergraduate Students in University Malaysia Perlis: A Conceptual Paper