AI can help life sciences organizations improve efficiencies across the value chain, from research and development to clinical trials to patient engagements. AI has truly become a game-changer for the life sciences industry in recent years. The global artificial intelligence (AI) in life sciences market size was exhibited at USD 2.50 billion in 2023 and is projected to hit around USD 15.45 billion by 2033, growing at a CAGR of 19.98% during the forecast period 2024 to 2033.
We asked our experts what more they think we will see in 2025. Here is what they had to say. And check out all our prediction posts looking to 2025
Isaac Bentwich, MD, Founder & CEO, Quris-ai
LinkedIn: Isaac Bentwich, MD
AI will help us live longer AND healthier
Over the past 50 years, healthcare and medicine have made incredible advances, adding years to the average lifespan. However, this progress has not extended our health-span – the years lived strong, healthy, and free of disease. Simply put, while we get to live longer, we don’t get to live better longer. Can AI, which has already transformed so many aspects of our lives, step in to close this gap and help us live longer AND healthier?
In 2025, the answer might be yes. One exciting possibility is the Bio-AI twin approach, which uniquely combines a person’s biology with AI. For instance, with Patient-on-Chip technology–created from an individual’s induced pluripotent stem-cells (iPSCs)–we can optimize drug safety and dosing, and predict harmful interactions between otherwise safe drugs. In addition, such Bio-AI twins can be preserved in iPSC stem-cell banks to unlock future therapeutic possibilities for organ regeneration and auto-transplantation. Integrating AI and biology could be a game changer in 2025, offering predictive, preventive, and personalized healthcare solutions that support the aging process, ensuring our lives aren’t just longer, but healthier.
2025 will mark a turning point in reducing our reliance on animal testing
Animal testing has long been deemed ineffective, with 92% of drugs that pass these tests ultimately failing in human trials. However, 2025 is set to mark a turning point in reducing animal testing in drug development.
Two key factors are driving this shift. First, cutting-edge AI combined with significant advancements in stem-cell and Organ-on-Chip technologies now offer superior drug safety and efficacy predictions. Second, legislative changes, such as the FDA Modernization Acts 2.0 and 3.0, are actively pushing for the adoption of these technologies, steering us away from animal testing. These advancements represent a critical shift toward drug development methods that are not only more ethical and efficient, but also more accurate and personalized than ever before.
2025 will bring foundational models for health
In the last two years, foundational models, like large language models (LLMs), have revolutionized our lives and reshaped many industries. But developing an effective foundational model for health is more challenging, because it must be directly trained on biological data rather than text-based data from medical literature and research papers.
In 2025, we will likely see foundational models for health thanks to breakthroughs in creating 3D-biology datasets from large-scale experiments on miniaturized human organs-on-chips. This cutting-edge technology has already demonstrated impressive accuracy in predicting drug safety compared to existing methods and also captures patient diversity and multi-organ response – a game changer for personalized medicine. However, the impact of these models extends beyond healthcare, with the potential to transform industries such as insurance, wellness, and longevity, paving the way for exciting new opportunities.
Walker Bradham, Product Management & Design, Zelta
LinkedIn: Walker Bradham
Much of the conversation around artificial intelligence in the clinical trials space has been around generative AI and large language models. That certainly makes sense – models like ChatGPT and Llama are exciting and offer interesting new avenues for automation, especially when it comes to enhancing clinical trial processes – but they aren’t quite ready for prime time in our industry just yet.
Instead, as we move into 2025, supervised machine learning (ML) will prove itself as the more targeted approach to creating value and return on investment for AI applications in clinical trials. This fit-for-purpose approach will enable clinical data managers, researchers, and clinicians to create more immediate, repeatable, and valuable outcomes in their clinical trial processes.
Examples include:
- Automated configuration of electronic data capture (EDC) systems based on digital protocols, organization standards, and biomedical concepts, a time-saving approach to digitize workflows within clinical trials.
- Workflow enhancements enabled by AI and supervised ML to reduce tedious manual work study design validation, biostatistical annotations, and clinical data review.
- ML assistance for the typically laborious work to structure data, i.e., Medical Coding, range normalization, and unit conversions.
These innovative supervised ML applications will give clinicians, researchers, and administrators of clinical trials greater confidence and control over their outcomes.
Aaron Brauser, CEO and Co-Founder, Realyze Intelligence
LinkedIn: Aaron Brauser
In 2025, AI will revolutionize clinical trial management by significantly enhancing patient identification and matching processes. AI algorithms will analyze vast datasets, including electronic health records (EHRs) and genetic information, to identify potential trial candidates with unprecedented accuracy and speed. This will ensure that patients are matched to the most appropriate clinical trials based on their unique profiles, leading to more efficient recruitment and better trial outcomes.
Fran Brown, SVP, Drug Development Solutions, Certara
LinkedIn: Fran Brown
In silico modeling and prediction tools enabled by AI and machine learning will continue to expand and propagate in 2025. There’s a lot of interest in technologies that can reduce the amount of in vivo animal testing during drug development programs, and in 2025, we’ll increasingly see the ability to leverage in silico modeling to better focus the in vivo work that is needed. A good example of this is around first-in-human (FIH) dose predictions. Quantitative systems pharmacology (QSP) modeling can supplement areas where conventional techniques are either inaccurate or unfeasible (i.e., bi-specific and tri-specific antibodies) to provide a better estimate of a starting dose to reduce the number of dose levels going into FIH studies. AI-enabled use cases for model-informed drug development are expanding rapidly and will continue to do so in the years to come.
Ryan Brown, Regional VP of Trial Landscape, H1
LinkedIn: Ryan B.
Using AI to Combat Rare Diseases
With nearly 1,000 AI-enabled devices approved, the FDA has strongly supported the use of AI in medicine. In 2025, we will see AI leveraged for faster, more accurate diagnoses in rare disease cases, reducing the rate of more than a quarter of rare disease patients who spend 7+ years until they receive a correct diagnosis (up from 15% from 30 years ago). By connecting symptom patterns and medical histories across dispersed datasets, clinicians will identify rare conditions sooner, reducing the time to diagnosis by years and drastically improving outcomes.
Beyond diagnosis, AI will predict treatment responses, personalize therapies, and uncover new disease patterns. And, in rare diseases where patient populations are limited, AI’s ability to streamline trial design and identify patients most likely to benefit from specific therapies will further accelerate rare disease treatment.
Robyn Coward, Director, Life Sciences, QAD.com
LinkedIn: Robyn Coward
The pharmaceutical industry has been a frontrunner in adopting the use of AI for use in drug discovery, development and design, clinical trials, and post-market surveillance.
The use of AI for additional pharma use cases has continued to grow even as AI legislation has been enacted or planned. During 2024, the EU Regulation 24/1689 passed in March 2024 (EU AI Act), which targeted all industries — and the FDA continued to collaborate with industry to develop guidelines and a regulatory framework for the safe implementation of AI in the development of pharmaceuticals.
In light of the recent presidential election in the US, observers of the president-elect’s past actions and campaign promises note that it is highly likely that there will be a reduction in the FDA’s regulatory oversight of AI, and a relaxation of the current AI reporting rules. Some speculate whether public trust will decrease if it is perceived that the president-elect’s deregulation proposals will result in fewer safeguards for AI-driven drug research and development.
Though the evolution of the AI regulatory landscape will continue, it will most likely not deter pharmaceutical companies’ continued innovative experimentation with the use of AI. However, beyond a shift in federal government oversight of AI and the high likelihood of the reversal of the policies of the current administration, is the potential for very significant impacts that proposed shifts in trade and import regulations are likely to have on the pharmaceutical supply chain. With such sweeping proposals anticipated, it will be especially important for the pharmaceutical sector to increase resilience and agility to navigate the geopolitical and economic impacts expected to come in the near future.
Mirit Eldor, Managing Director, Life Sciences Solutions, Elsevier
LinkedIn: Mirit Eldor
General-purpose AI models are unable to meet the needs of the scientific community, which cannot run the risk of hallucinations or out-of-context predictions which can be simply wrong, and lead to expensive mistakes, such as launching clinical trials with a compound where the likelihood of success has been wrongly predicted. To counteract this risk, retrieval augmented generation architectures (RAG) will become the norm for AI use in sensitive industries. By limiting the data Generative AI tools can draw from to context and domain-specific sources, researchers will be able to ensure outputs generated by AI are accurate and trustworthy.
Ariel Katz, CEO & Co-Founder, H1
LinkedIn: Ariel Katz
Expanded Use Cases for AI in Clinical Trial Operations
In 2025, we will see companies leverage AI for new use cases to streamline operations, reduce timelines and costs, and improve the likelihood of successful trials. As more clinical ops teams embrace AI, barriers to clinical trial participation will be reduced. AI’s capabilities to integrate and analyze diverse data sources will lead to more precise patient matching and trial design. Its advanced analytics will predict drop-out rates and forecast adverse effects, and tools like chatbots and personalized apps will enhance engagement and adherence throughout the trial. Once trials are underway, teams will use AI to analyze results, make real-time adjustments based on incoming data, and eventually draft regulatory submissions.
Vaibhav Kulkarni, PhD, Data Engineering Lead, Debiopharm
LinkedIn: Vaibhav Kulkarni
Advancements in AI are set to revolutionize the life sciences sector in 2025, particularly in clinical trial management and drug discovery and development. AI-powered models enable the rapid simulation of complex biological processes, significantly reducing research timelines—a breakthrough recognized by the Nobel Prize this year. AI platforms facilitate virtual screening of potential drug candidates, expediting the identification of promising molecules and paving the way for faster, more innovative therapeutic breakthroughs. The integration of quantum computing offers unprecedented computational power to tackle complex challenges. By 2025, these advancements will converge with increasingly sophisticated datasets, enabling life sciences companies to uncover new therapeutic pathways and reduce time-to-market for critical treatments. As these technologies mature, they will catalyze a shift toward more efficient, data-driven approaches, fundamentally reshaping global healthcare delivery.
Melvin Lai, Senior Associate, Silicon Foundry
LinkedIn: Melvin Lai
The life sciences sector has been among the lagging industries in the post-Covid era, but is expected to outperform in the coming year as capital market conditions continue to improve and a window for IPOs and M&A exits spurs further positive momentum.
AI for Clinical Trials: Technologies using AI, data analytics, and digital platforms will continue to emerge to streamline clinical trials by improving processes, data quality, and patient engagement.
Shaheen Lakhan, MD, PhD, FAAN, Chief Medical Officer, Click Therapeutics
LinkedIn: Shaheen E Lakhan, MD, PhD, FAAN
It won’t be long before every pharma company starts the process of digitizing their pipelines. When the FDA released its draft guidance on Prescription Drug Use-Related Software (PDURS) in 2023, it allowed for any added clinically meaningful benefit, from the use of software together with a drug, to be added directly to the drug label. Since then, we’ve seen increased interest from companies looking to add software-enhanced (SE) drug therapies to their portfolios.
Next year, we’ll begin to see more development of smart ‘SE’ formulations of drugs to improve treatment outcomes. These software-enhanced drug therapies combine a highly validated digital therapeutic with traditional pharmacotherapy to offer patients unique benefits including increased drug efficacy, optimized dosing, agile side effect management, and enhanced tolerability and persistence.
These treatments are set to transform the healthcare industry by leveraging the latest technology to respond and adapt to patients to create personalized digital treatment journeys. The ability to access these treatments anywhere from a smartphone can help bridge gaps in treatment and integrate seamlessly into pharmaceutical care.
George Lazenby, CEO and Co-Founder, OrderInsite
LinkedIn: George Lazenby
By the end of next year, I anticipate AI-driven inventory control systems will become the backbone of pharmacy supply chain management. These systems will not only predict demand and minimize stockouts but also optimize inventory levels with unprecedented accuracy. We’re already seeing AI solutions identify prescription drug shortages, allowing pharmacies to seamlessly source stock from alternate suppliers. By analyzing historical data, local demand, and market trends, AI will help pharmacies strike the perfect balance—minimizing both overstocking and understocking to ensure a smoother replenishment process. Cloud-based platforms will further revolutionize this by centralizing inventory management across multiple locations, offering real-time visibility and faster, more informed decision-making. With drug shortages expected to persist into 2025 and beyond, pharmacies that embrace these advancements will be better positioned to ensure patients have access to critical medications when they need them most.
Christopher McSpiritt, Head of Life Sciences, Domino Data Lab
LinkedIn: Christopher M. McSpiritt
AI-Driven Drug Discovery Will Graduate from PoC to Practical Applications: AI-driven drug discovery will transition from proof-of-concept to practical applications, particularly in the generation and testing of molecular compounds in silico. As algorithms continue to improve in simulating biological interactions, we can expect a significant acceleration in the drug discovery process. This shift will not only enhance the efficiency of early testing but also pave the way for innovative treatments, ultimately revolutionizing the pharmaceutical landscape.
AI Will Accelerate Clinical Trial Recruiting for Faster Medical Innovation: By 2025, AI models will harness electronic health records (EHRs) and real-time patient data to streamline the recruitment process for clinical trials, enabling the identification of eligible candidates with unmatched accuracy and speed. Evidence of this transformation is already emerging, with AI-driven platforms significantly reducing recruitment timelines by efficiently matching patients to trial criteria. These advancements promise to reshape the recruitment landscape, enhancing trial effectiveness and accelerating the pace of medical innovation.
Courtney Noah, PhD, VP, Scientific Affairs, BioIVT
LinkedIn: Courtney Noah, PhD
Looking ahead to 2025, AI and machine learning are set to tackle some of the biggest challenges in life sciences, changing how we approach healthcare. In biomarker detection, AI will help sift through large datasets, improving accuracy and making it easier to turn complex data into actionable insights that track disease onset and progression more effectively. Critical to driving success will be access to highly standardizing specimen collections with clean data sets to enable accurate insights. On the treatment side, AI will further enhance personalized care. Predictive analytics will enable doctors to identify trends in a patient’s medical history, leading to more tailored and effective treatment plans. In addition, AI insights can complement the expertise of pathologists and provide workflow efficiencies to improve diagnostic lead times. All these advancements will come together to create a healthcare system in 2025 that is more precise, personalized, and efficient than ever before.
Nish Parekh, Senior Vice President, Chief Product, Omnicell
LinkedIn: Nish Parekh
AI and Machine Learning Drive Operational Efficiency
AI and machine learning are poised to be the game-changers in pharmacy operations over the next few years. The market for AI in healthcare could grow to $17.2 billion by 2032 and these technologies will automate routine tasks, help predict patient needs, and drive operational efficiency, allowing pharmacy teams to focus on higher-value tasks that impact patient care. The true promise of AI in pharmacy is accuracy, efficiency, and the ability to optimize inventory management and operations, making care safer, ensuring a stable medication inventory, and ability to support more personalized therapies. This will redefine how pharmacies operate and interact with patients.
Raviv Pryluk, PhD, CEO and Co-founder, PhaseV
LinkedIn: Raviv Pryluk
2025 will see AI and machine learning continue to impact clinical trials, solving key pain points, such as heterogeneous response to treatments, recruitment challenges, and the increasing cost and duration of clinical development programs. Following the significant advances made in oncology, AI tools will be implemented across more clinical development domains, including neurodegenerative conditions, immunology, nephrology, and rare diseases. Amid this expansion and the growing role of precision medicine in drug development, the FDA and other regulatory bodies will continue to work collaboratively with trial sponsors and innovators to pave the way for more precise, ethical, and patient-centric clinical AI tools.
Marc Samuels, CEO and President, ADVI Health
We expect AI use in clinical drug discovery to continue to thrive in both drug target selection as well as opportunity for better and more real world evidence; however, our eyes are on the use of AI for market access uses, first in forecasting and market sizing and then in downstream impacts of IRA and PDABs.
Rafael Sidi, Senior Vice President & General Manager of Health Research, Wolters Kluwer Health
LinkedIn: Rafael Sidi
The role of AI in publishing and medical research is evolving to enhance, not replace, human expertise. Researchers are seeking solutions that meaningfully support their workflows. We anticipate a growing adoption of AI tools designed to screen manuscripts for plagiarism, ensure adherence to journal guidelines, and improve language quality. These tools will emphasize transparency, trust, and authorship integrity, ensuring equitable access for researchers globally—a critical factor in driving impactful and inclusive medical research. In addition to transforming peer review by streamlining the process of matching manuscripts with suitable reviewers, AI will assist researchers in upholding standards of fairness, minimizing bias, and maintaining a commitment to ethical publishing. AI will also play a crucial role in data analysis, helping identify trends and insights that drive innovation and improve patient outcomes. In terms of dissemination, AI will optimize research content for diverse platforms and audiences, ensuring valuable findings are delivered to the right people efficiently. Enhanced searchability and accessibility will empower researchers and practitioners to quickly locate relevant information, amplifying the practical impact of medical research While challenges remain—such as safeguarding data privacy and addressing ethical concerns—the positive impact of AI will become increasingly evident in 2025. The focus will be on integrating AI in ways that complement human skills, fostering collaboration where technology and expertise converge to advance medical research and publishing. The future of medical publishing lies in embracing these opportunities thoughtfully, ensuring AI becomes a powerful enabler of innovation, accessibility, and ethical excellence.
Andrew Stelzer, Head of Business Development, Unlearn
LinkedIn: Andrew Stelzer
In 2025, healthcare leaders will face a clear choice: embrace AI or risk falling irreversibly behind. The status quo no longer works in an era where the pace of innovation is accelerating, and the stakes for patients have never been higher. AI is not just a tool for operational efficiencies—it’s a transformative force driving program-wide innovation. By reimagining how we design and conduct clinical trials, AI is enabling faster, more precise clinical development, ultimately getting life-saving therapies to patients sooner. Companies that recognize this shift and act decisively will define the future of our industry, gaining a competitive edge. Those that hesitate risk not only their market position but their relevance in a rapidly evolving landscape.
Erik Terjesen, Partner, Silicon Foundry
LinkedIn: Erik Terjesen
Leveraging cutting edge AI to advance drug discovery:
- AI-driven solutions will continue to be in focus for pharma and life sciences companies as they seek to streamline their development processes and shrink time to market
- As major pharma companies monitor how emerging AI/ML solutions can augment their existing processes, organizations should evaluate whether it is better to invest in building their own internal AI tool for clinical protocol design or opt for a third party SaaS solution Several factors need to be considered
- Using Emerging Tools to enhance internal AI capabilities based on an emerging AI/ML company’s core technology presents a long term strategic play with more flexibility and potential for tailored solutions that align closely with a company’s specific needs This approach provides an extended runway for innovation and future scalability, but requires capable in house talent to effectively customize the tools, potentially stretching a company’s resources and leading to operational inefficiencies as internal capacities are strained
- Leveraging a SaaS Provider offers quicker implementation and may have a proven track record, but might not provide the same level of customization or adaptability in the long run
- The decision should hinge on the company’s current resources, long term vision, and immediate needs
Becky Upton, President at not-for-profit The Pistoia Alliance
LinkedIn: Becky Upton
In 2025, as AI gains ground in R&D, drug discovery, and patient diagnostics, pharma companies will prioritize explainability to address bias and build trust in AI systems. The Pistoia Alliance’s 2024 Lab of the Future report highlights industry apprehensions around AI that may slow adoption; 41% of life science professionals cite privacy and security concerns around sensitive data in AI models, and 28% cite AI’s perception as not trustworthy, reliable, or responsible. To address these concerns, in 2025 life science companies will enhance model explainability, ensuring AI insights are transparent, secure, and fair in both drug development and personalized treatment.
Manuela Vecsler, PhD, VP of Clinical and Scientific Affairs, Ibex Medical Analytics
LinkedIn: Manuela Vecsler
In 2025, AI is poised to impact oncology by advancing precision medicine, enabling tailored treatment strategies that cater to the unique needs of each patient. Specifically for breast cancer diagnostics, advanced AI-powered tools will help pathologists achieve more accurate, objective, and standardized assessments of critical biomarkers like HER2, ER, PR, and Ki-67, improving diagnostic accuracy and treatment decisions. For the treatment of HER2-expressing breast cancer in particular, reduced subjectivity in IHC scoring as AI is integrated into clinical workflows will enable more accurate identification of patients eligible for emerging HER2-targeted therapies. AI-based biomarkers are also emerging as new ways to personalize targeted treatment for patients. Looking ahead, AI is on a trajectory to provide sustained innovation and transformation in life sciences as adoption expands and as healthcare and pharma converge.
Michael Young, Co-Founder, Lindus Health
LinkedIn: Michael Young
The FDA will become increasingly assertive on implementing stricter diversity requirements in clinical trials. We’ve seen this already with the FDA’s latest draft guidance released in June, mandating that Phase 3 and pivotal studies have diversity action plans in place to improve enrollment of diverse populations. This would be especially beneficial for early phase trials, however, where it’s important to understand the safety of a treatment on different ethnic groups.