International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 73 | Issue 7 | Year 2025 | Article Id. IJCTT-V73I7P101 | DOI : https://doi.org/10.14445/22490183/IJCTT-V73I7P101

The Rise of Foundation Models in Industry: A Cross Domain Survey of LLM Applications in Healthcare, Finance, Legal, and Education

Chaithanya Reddy Bogadi

Received Revised Accepted Published
26 May 2025 18 Jun 2025 10 Jul 2025 27 Jul 2025

Citation :

Chaithanya Reddy Bogadi, "The Rise of Foundation Models in Industry: A Cross Domain Survey of LLM Applications in Healthcare, Finance, Legal, and Education," International Journal of Computer Trends and Technology (IJCTT), vol. 73, no. 7, pp. 1-15, 2025. Crossref, https://doi.org/10.14445/22490183/IJCTT-V73I7P101

Abstract

Large Language Models (LLMs) transform artificial intelligence applications across industries. This paper presents the DOME (Domain-Operation-Model-Evaluation) framework, a novel taxonomy for systematically defining and assessing industrial LLM implementations across healthcare, finance, legal services, and education sectors. Through systematic analysis of published case studies, this research identifies domain adaptability patterns and evaluates performance criteria across sectors. Results reveal significant maturity variations: healthcare and finance demonstrate advanced implementations, while legal and Education sectors remain largely experimental. The study identifies critical deployment constraints, including hallucinations, domain adaptation complexity, and regulatory barriers. Key emerging trends include retrieval-augmented generation systems, domain-specific fine-tuning, and edge computing paradigms. The framework provides researchers and practitioners with systematic guidelines for responsible AI deployment in regulated industries, advancing the field’s understanding of effective cross-domain LLM implementation strategies. 

Keywords

Artificial intelligence, Domain adaptation, Evaluation framework, Foundation models, Large language models.

References

[1] Daron Acemoglu, and Pascual Restrepo, “Tasks, Automation, and The Rise in US Wage Inequality,” Econometrica, vol. 90, no. 5, pp. 1973–2016, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Zabir Ai Nazi, and Wei Peng, “Large Language Models in Healthcare and Medical Domain: A Review,” Informatics, vol. 11, no. 3, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Amir Ali Rahsepar, “Large Language Models for Enhancing Radiology Report Impressions: Improve Readability While Decreasing Burnout,” Radiology, vol. 310, no. 3, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Jean-Baptiste Alayrac et al., “Flamingo: A Visual Language Model for Few-shot Learning,” Advances in Neural Information Processing Systems, vol. 35, 2022.
[Google Scholar] [Publisher Link]
[5] Emily Alsentzer et al., “Publicly Available Clinical BERT Embeddings,” arXiv Preprint, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Dogu Araci, “FinBERT: Financial Sentiment Analysis with Pre-trained Language Models,” arXiv preprint arXiv:1908.10063, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] David Baidoo-Anu, and Leticia Owusu Ansah, “Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Education,” Journal of AI, vol. 7, no. 1, pp. 52-62, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Bank of America, Erica, The Guide by Your Side, is Here for You. [Online]. Available: http://info.bankofamerica.com/en/digital banking/erica
[9] Yu Gu et al., “Domain-specific Language Model Pretraining for Biomedical Natural Language Processing,” ACM Transactions on Computing for Healthcare, vol. 3, no. 1, pp. 1–23, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Hao Liu, “PubMed GPT: a Domain-Specific Large Language Model for Biomedical Text,” Medium, 2023.
[Publisher Link]
[11] Rishi Bommasani et al., “On the Opportunities and Risks of Foundation Models,” arXiv preprint arXiv:2108.07258, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Tom Brown et al., “Language Models are Few-shot Learners,” Advances in Neural Information Processing Systems, 2020.
[Google Scholar] [Publisher Link]
[13] Sebastian Bubeck et al., “Sparks of Artificial General Intelligence: Early Experiments with GPT-4,” arXiv preprint arXiv:2303.12712, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Nicholas Carlini et al., “Extracting Training Data from Large Language Models,” USENIX Security Symposium, 2021.
[Google Scholar] [Publisher Link]
[15] John M. Polson, and Evan Shenkman, BREAKING NEWS: Casetext’s CoCounsel is Powered by OpenAI’s GPT-4, Fisher Phillips, 2023.
[Publisher Link]
[16] Llias Chalkidis et al., “LEGAL-BERT: The Muppets Straight out of Law School,” arXiv Preprint, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Zhiyu Chen et al., “FinQA: A Dataset of Numerical Reasoning Over Financial Data,” arXiv Preprint, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Lingjiao Chen, Matei Zaharia, and James Zou, “FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance,” arXiv preprint arXiv:2305.05176, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Aakanksha Chowdhery et al., “PaLM: Scaling Language Modeling with Pathways,” JMLR, 202.
[Google Scholar] [Publisher Link]
[20] M. Chui, J. Manyika, and M. Miremadi, “Notes from the AI Frontier: Applications and Value of Deep Learning,” McKinsey Global Institute, 2018.
[Publisher Link]
[21] Cognii, Cognii Virtual Learning Assistant. [Online]. Available: https://www.cognii.com/technology
[22] LeAh, ContractPod Ai, Automation and AI are your Partners in Strategic Excellence. [Online]. Available: https://contractpodai.com/department/legal/
[23] Jiaxi Cui et al., “ChatLaw: Open-source Legal Large Language Model with Integrated External Knowledge Bases,” CoRR, 2023.
[Google Scholar] [Publisher Link]
[24] Stan Benjamens, Pranavsingh Dhunnoo, and Bertalan Mesko, “The State of Artificial Intelligence-based FDA-approved Medical Devices and Algorithms: An Online Database,” NPJ Digital Medicine, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Jacob Devlin et al., “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 4171-4186, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Duolingo, “Duolingo Max” Shows the Future of AI Education, 2023. [Online]. Available: https://investors.duolingo.com/news releases/news-release-details/duolingo-max-shows-future-ai-education
[27] Aniket Deshpande, “Regulatory Compliance and AI: Navigating the Legal and Regulatory Challenges of AI in Finance,” 2024 International Conference on Knowledge Engineering and Communication Systems, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Sebastian Gehrmann et al., “Understanding and Mitigating Risks of Generative AI in Financial Services,” arXiv:2504.20086, 2025.
[CrossRef] [Publisher Link]
[29] Sara Gerke, Timo Minssen, and Glenn Cohen, “Ethical and Legal Challenges of Artificial Intelligence-driven Healthcare,” Artificial Intelligence in Healthcare, pp. 295–336, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Ali Zeb, Rafid Ullah, and Rehmat Karim, “Exploring the Role of ChatGPT in Higher Education: Opportunities, Challenges and Ethical Considerations,” International Journal of Information and Learning Technology, vol. 41, no. 1, pp. 99-111, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Gradescope. [Online]. Available: https://www.gradescope.com/?locale=es es#:~:text=Gradescope%20helps%20you%20seamlessly%20administer,how%20your%20students%20are%20doing
[32] Neel Guha et al., “LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Model,” arXiv: 2308.11462, 2023.
[CrossRef] [Publisher Link]
[33] Kate Rattray, Clio, Harvey AI for Legal Professionals: Features, Benefits and More. [Online]. Available: https://www.clio.com/blog/harvey-ai-legal/
[34] Peter Henderson et al., “Pile of Law: Learning Responsible Data Filtering from The Law and a 256GB Open-source Legal Dataset,” Advances in Neural Information Processing Systems, 2022.
[Google Scholar] [Publisher Link]
[35] Jordan Hoffmann et al., “Training Compute-optimal Large Language Models,” arXiv preprint arXiv:2203.15556, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Antonio J.G. Busson et al., “Saturn Platform: Foundation Model Operations and Generative AI for Financial Services,” arXiv:2312.07721, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Alon Jacovi et al., “Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI,” Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 624–635, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Ziwei Ji et al., “Survey of Hallucination in Natural Language Generation,” ACM Computing Surveys, vol. 55, no. 12, pp. 1–38, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Bahar Memarian, and Tenzin Doleck, “Fairness, Accountability, Transparency, and Ethics (FATE) in Artificial Intelligence (AI) and Higher Education: A Systematic Review,” Computers and Education: Artificial Intelligence, vol. 5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Razvan V. Marinescu et al., “Disease Knowledge Transfer Across Neurodegenerative Diseases,” Medical Image Computing and Computer Assisted Intervention-MICCAI 2019, pp. 860-868, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[41] Tyler Haberle et al., “The Impact of Nuance DAX Ambient Listening AI Documentation: A Cohort Study,” Journal of the American Medical Informatics Association, vol. 31, no. 4, pp. 975-979, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[42] J.P. Morgan, Quest IndexGPT: Harnessing Generative AI for Investable Indices, 2024. [Online]. Available: https://www.jpmorgan.com/insights/markets/indices/indexgpt
[43] Enkelejda Kasneci et al., “ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education,” Learning and Individual Differences, vol. 103, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[44] Harvard Business Review, A New Survey Reveals How Legal Professionals Expect AI to Impact Their Work, 2023. [Online]. Available: https://hbr.org/sponsored/2023/12/a-new-survey-reveals-how-legal-professionals-expect-ai-to-impact-their-work
[45] Khan Academy, Teachers Share Why Our AI-Powered Teaching Assistant, Khanmigo, Is a Must-Have, 2024. [Online]. Available: https://blog.khanacademy.org/teachers-share-why-our-ai-powered-teaching-assistant-khanmigo-is-a-must-have/
[46] Shanshan Han, “Bridging Today and The Future of Humanity: AI Safety in 2024 and Beyong,” arXiv:2410.18114, 2024.
[CrossRef] [Google Scholar] [Publisher Link] 
[47] Yaniv Leviathan, Matan Kalman, and Yossi Matias, “Fast Inference from Transformers Via Speculative Decoding,” Proceedings of the 40th International Conference on Machine Learning, pp. 19274–19286, 2023.
[Google Scholar] [Publisher Link]
[48] Patrick Lewis et al., “Retrieval-augmented Generation for Knowledge-intensive NLP Tasks,” Advances in Neural Information Processing Systems, vol. 33, 2020.
[Google Scholar] [Publisher Link]
[49] Rylan Schaeffer et al., “Correlating and Predicting Human Evaluations of Language Models from Natural Language Processing Benchmarks,” arXiv:2502.18339, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[50] Yunxiang Li et al., “ChatDoctor: A Medical Chat Model Fine Tuned on a Large Language Model Meta-AI (LLaMA) using Medical Domain Knowledge,” Cureus, vol. 15, no. 6, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[51] The Alan Turing Institute, Leveraging Large Language Models in Finance: Pathways to Responsible Adoption.
[Publisher Link]
[52] Hang Yuan, Saizhuo Wang, and Jian Guo, “Alpha-GPT 2.0: Human-in-the-Loop AI for Quantitative Investment,” arXiv:2402.09746, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[53] Gagan Bhatia et al., “FinTral: A Family of GPT-4 Level Multimodal Financial Large Language Models,” arXiv:2402.10986, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[54] Nelson F. Liu et al., “Lost in the Middle: How Language Models use Long Contexts,” arXiv preprint arXiv:2307.03172, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[55] Pengfei Liu et al., “Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing,” ACM Computing Surveys, vol. 55, no. 9, pp. 1–35, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[56] Renqian Luo et al., “BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining,” Briefings in Bioinformatics, vol. 23, no. 6, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[57] LexisNexis, Lexis+ AI: Transform Your Legal Work. [Online]. Available: https://www.lexisnexis.com/en-us/products/lexis-plus-ai.page
[58] Sewon Min et al., “FactScore: Fine-grained Atomic Evaluation of Factual Precision in Long-form Text Generation,” arXiv preprint arXiv:2305.14251, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[59] Ethan Mollick, and Lilach Mollick, “Assigning AI: Seven Approaches for Students, with Prompts,” arXiv Preprint, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[60] OpenAI, Morgan Stanley Uses AI Evals to Shape the Future of Financial Services. [Online]. Available: https://openai.com/index/morgan-stanley/
[61] Harsha Nori et al., “Capabilities of GPT-4 on Medical Challenge Problems,” arXiv preprint arXiv:2303.13375, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[62] OpenAI, “GPT-4 Technical Report,” arXiv preprint arXiv:2303.08774, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[63] Matthew E. Peters et al., “Deep Contextualized Word Representations,” arXiv Preprint, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[64] Wayground, Quizizz AI. [Online]. Available: https://webflow-dev.quizizz.com/quizizz-ai?source=ai-solution-btn&lng=en
[65] Inioluwa Deborah Raji et al., “Closing the AI Accountability Gap: Defining an End-to-end Framework for Internal Algorithmic Auditing,” Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 33–44, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[66] Badhan Chandra Das, M. Hadi Amini, and Yanzhao Wu, “Security and Privacy Challenges of Large Language Models: A Survey,” arXiv:2402.008888, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[67] Taeyoon Kwon et al., “Large Language Models Are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt Generated Rationales,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 16, pp. 18417-18425, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[68] Rasha Halat, and Lina Khair Rahme, Addressing Inequities in Education: AI as a Double-Edged Sword (Part II), Middle East Professional Learning Initiative, 2024.
[Publisher Link]
[69] Timo Schick et al., “Toolformer: Language Models can Teach Themselves to Use Tools,” Advances in Neural Information Processing Systems, 2023.
[Google Scholar] [Publisher Link]
[70] Edward H. Shortliffe, and Martin J. Sepúlveda, “Clinical Decision Support in the Era of Artificial Intelligence,” JAMA, vol. 320, no. 21, pp. 2199–2200, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[71] Karan Singhal et al., “Towards Expert-level Medical Question Answering with Large Language Models,” Nature Medicine, vol. 31, pp. 943-950, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[72] Spellbook, AI in Legal Departments: 2025 Benchmarking Report. [Online]. Available: https://www.spellbook.legal/blog/counselwell
[73] Nandan Thakur et al., “BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models,” arXiv preprint, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[74] Arun James Thirunavukarasu et al., “Large Language Models in Medicine,” Nature Medicine, vol. 29, pp. 1930–1940, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[75] Thomson Reuters, A Year in Review: How AI Transformed the Legal Profession in 2023. [Online]. Available: https://legal.thomsonreuters.com/blog/how-ai-transformed-the-legal-profession-in-2023/
[76] Hugo Touvron et al., “LLaMA: Open and Efficient Foundation Language Models,” arXiv preprint arXiv:2302.13971, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[77] Scott W. Perkins et al., “Improving Clinical Documentation with Artificial Intelligence: A Systematic Review,” Advances in Health Information Science and Practice, vol. 21, no. 2, 2024.
[Publisher Link]
[78] Richard M. Schwartzstein, “Clinical Reasoning and Artificial Intelligence: Can AI Really Think?,” Transaction of the American Clinical and Climatological Association, vol. 134, pp. 133-145, 2024.
[Google Scholar] [Publisher Link]
[79] Brady D. Lund et al., “ChatGPT and a New Academic Reality: AI-Written Research Papers and the Ethics of the Large Language Models in Scholarly Publishing,” Journal of the Association for Information Science and Technology, 2023.
[Google Scholar] [Publisher Link]
[80] Yiqiu Shen et al., “ChatGPT and Other Large Language Models are Double-edged Swords,” Radiology, vol. 307, no. 2, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[81] Lei Wang et al., “A Survey on Large Language Model based Autonomous Agents,” Frontiers of Computer Science, vol. 18, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[82] Alaa Abd-alrazaq et al., “Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions,” Medical Education, vol. 9, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[83] Jason Wei et al., “Chain of thought Prompting Elicits Reasoning in Large Language Models,” Advances in Neural Information Processing Systems, vol. 35, 2022.
[Google Scholar] [Publisher Link]
[84] Jason Wei et al., “Emergent Abilities of Large Language Models,” arXiv preprint, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[85] Alexander Wei, Nika Haghtalab, and Jacob Steinhardt, “Jailbroken: How Does LLM Safety Training Fail?,” Advances in Neural Information Processing Systems, 2023.
[Google Scholar] [Publisher Link]
[86] Laura Weidinger et al., “Ethical and Social Risks of Harm from Language Models,” arXiv preprint arXiv:2112.04359, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[87] Jonathan H. Choi, Amy B. Monahan, and Daniel Schwarcz, “Lawyering in the Age of Artificial Intelligence,” Minn. L. Rev, 2024.
[Google Scholar] [Publisher Link]
[88] Malik Sallam, “ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns,” Healthcare, vol. 11, no. 6, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[89] Qingyun Wu et al., “AutoGen: Enabling Next-gen LLM Applications Via Multi-agent Conversation,” arXiv preprint arXiv:2308.08155, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[90] Shijie Wu et al., “BloombergGPT: A Large Language Model for Finance,” arXiv preprint arXiv:2303.17564, 2023.
[Google Scholar] [Publisher Link]
[91] Xiang Yue et al., “Mammoth: Building Math Generalist Models through Hybrid Instruction Tuning,” arXiv preprint arXiv:2309.05653, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[92] Wei Luo, and Dihong Gong, “Pre-trained Large Language Models for Financial Sentiment Analysis,” arXiv:2401.05215, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[93] Nestor Maslej et al., “Artificial Intelligence Index Report 2023,” arXiv preprint, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[94] Michael Chui et al., “The State of AI in 2023: Generative AI’s Breakout Year,” McKinsey Global Institute, McKinsey & Company, 2023.
[Google Scholar] [Publisher Link]
[95] Dan Hendrycks et al., “Measuring Massive Multitask Language Understanding,” arXiv Preprint, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[96] Yupeng Cao et al., “RiskLabs: Predicting Financial Risk Using Large Language Model Based on Multi-Sources Data,” arXiv:2404.07452, 2024.
[Google Scholar] [Publisher Link]