2025 saw major impacts in life science with the rise of generative AI in drug discovery, new global AI regulations, breakthroughs in precision medicine, and accelerated biotech innovation. These shifts reshaped how therapies are discovered, developed, and delivered, while also introducing new risks around compliance, data, and cybersecurity. We asked our experts how will the industry build on this momentum in 2026? Here is what they had to say.
And check out all our prediction posts looking to 2026.
Dr. Parmjot Bains, CEO, ImpediMed
LinkedIn: Dr Parmjot Bains, MD, M Phil, GAICD
As health systems increasingly prioritize patient experience, meaningful ROI will depend on digital health investments that deliver both clinical and operational value. Advances like bioimpedance spectroscopy (BIS) are ushering in a new era of precision in patient care. By continuously tracking changes in fluid, muscle, and fat composition, clinicians can intervene earlier and personalize care across oncology, cardiology, and metabolic health. Moving beyond one-time diagnostics to dynamic, whole-body measurement, digital tools are beginning to show how they can improve safety, satisfaction, and outcomes.
Eden Ben, CEO, Amorphical
LinkedIn: Eden Ben
2026: A Renaissance in Physical Science
The most transformative changes in biopharma often arrive quietly. In 2026, as funding tightens and demand for robust data increases, the focus will shift from spectacle to substance, from how fast we can build a model to how deeply we understand a mechanism. Expect a renaissance in physical science across drug discovery, as developers revisit structure, stability, and cellular environment as key levers of efficacy.
Lidia Bernik, MHS, MBA, President of Curation Solutions, MRO
LinkedIn: Lidia S. Bernik, MHS, MBA
Healthcare and life sciences have long worked in parallel, even though both struggle with slow, fragmented clinical data. Their shared need for faster, more precise insights is creating a powerful opportunity for closer collaboration and meaningful data exchange.
AI is accelerating that shift. While clinical trial activity has grown, timelines and costs remain too high. Pairing AI with rich clinical data sets and strong clinical expertise can shorten development cycles and bring new therapies to patients faster. I predict we’ll see more hospital and life sciences partnerships; supporting health system performance, accelerating trial enrollment, and ultimately expanding access for patient populations that previously lacked these opportunities.
Dr. Chris Bouton, Chief Technology Officer, Certara
LinkedIn: Christopher Bouton, Ph.D.
2025 saw a lot of regulatory turbulence, and biopharma has begun to see AI not just as a helpful accelerator, but a crucial tool to navigate regulatory uncertainty, which I expect to continue in the year ahead. With review standards shifting frequently, companies cannot afford to continue planning step-by-step anymore, so they’ll turn to advanced scenario modeling to continuously simulate how different evidence packages, study designs, or safety signals might play out. The real value of AI won’t just be speed; it’ll eliminating avoidable rework and keeping packages submission-ready even when the goalposts shift mid cycle. The organizations that stay resilient will be the ones using AI to predict pressure points before they happen, adjust timelines in real time, and protect efficiency without compromising scientific validity.
Emily Cook, Partner, McDermott Will & Schulte
LinkedIn: Emily Cook
We are expecting 2026 to be a very dynamic year for the 340B Program. In addition to the planned implementation of 340B rebate models and the intersecting implementation of the Medicare Drug Price Negotiation Program, we are watching numerous 340B-related litigation matters in federal and state courts across the country. We expect many of these to result in decisions during the first half of 2026 and may have a significant role in shaping the future of the 340B Program as to, among other things, the scope of manufacturer restrictions on sales of 340B drugs, the locations where 340B hospitals can use 340B drugs and the eligibility requirements for certain types of 340B covered entities. We are also anticipating material developments on the issue of who can receive 340B drugs, including potential related litigation.
Stacey Gilbert, MPH, MBA, Director, Precision Medicine, ADVI Health
LinkedIn: Stacey Gilbert, MPH, MBA
I believe 2026 could be the year of “perfect pairings”. Abbott’s acquisition of Exact Sciences is just the beginning! I see a wave of strategic lab acquisitions by pharmaceutical companies that have the focus and vision to jump into the oncology diagnostics market. Highly precise MRD and MCED tests are exploding onto the scene and building a groundswell for broader coverage because they are directly changing the way certain cancers are monitored and treated. It can be a smart, highly strategic move for pharma power players with the resources to innovate precision drug therapies in parallel with diagnostics companies to more fully suit the complex needs of patients living with cancer.
Conrad Gudmundson, Chief Commerical Officer, Lucem Health
LinkedIn: Conrad Gudmundson
True AI maturity involves connecting the logic of drug discovery with the mechanics of care delivery. In 2026, we expect platforms that don’t just identify molecules, but engineer their path into the provider’s workflow before the drug even launches. This end-to-end continuity, from R&D to point-of-care decision support, is what finally makes precision medicine scalable, ensuring that breakthrough therapies don’t just get approved, they get used.
Ali Jannati, MD, PhD, Director of Cognitive Science, Linus Health
LinkedIn: Ali Jannati, MD, PhD
AI will increasingly become the connective tissue linking clinical precision with human context when it comes to research. Advances in digital cognitive science are showing that AI can detect subtle cognitive changes and analyze patient-defined priorities to reveal insights long before symptoms appear. As these tools become more inclusive and broadly adopted, they will help move clinical research and healthcare beyond standardized testing toward truly individualized, equitable care that defines progress through earlier detection–but more importantly, the preservation of meaning and quality of life for individuals.
Greer Massey, PhD, Chief Scientific Officer, Molecular Designs
LinkedIn: Greer Massey
Diagnostics and health IT are converging in ways that will redefine how care teams use data. Molecular results are increasingly integrated into digital platforms that flag resistance risks and connect insights directly to clinical workflows. The future is about ensuring that the most relevant information reaches clinicians when and where they need it for patient care decisions.
David Minkin, President & General Manager, epocrates, athenahealth
LinkedIn: David Minkin
By 2026, pharma’s most durable advantage will come from credible, consent-driven data practices. As privacy rules multiply and patients demand clarity, companies that treat compliance as a strategic asset, rather than a hurdle will win. Expect leading brands to invest heavily in transparent data chains, trustworthy targeting, and clear consumer choice. Those that build visible, verifiable stewardship will convert compliance into confidence, creating a differentiator in a year when trust will be the tightest currency in healthcare.
Sade Mokuolu, PhD, Regional Business Development Manager – Life Sciences, Watson-Marlow Fluid Technology Solutions
LinkedIn: Sade Mokuolu, PhD
Increasingly stringent compliance regulations will accelerate the shift toward Quality as a Service (QaaS) in Life Sciences, moving focus from product reliability to end-to-end operational resilience. Priorities will revolve around risk reduction across the entire customer experience and minimizing production downtime. To keep pace, suppliers will be forced to move beyond products and features to offer robust supply chain solutions and ensure swift support for quality and regulatory compliance issues.
Courtney Noah, VP of Scientific Affairs, BioIVT
LinkedIn: Courtney Noah
Even with the rapid advancements in precision medicine and associated AI-applications, issues remain involving the quality and completeness of available data, interpretability of the models, and ethical considerations that demand robust regulatory frameworks. Addressing these issues requires collaborative efforts among clinicians, data scientists, regulators, and industry stakeholders.
The foundation for successful AI implementation lies in developing models based on high-quality, specimen-derived data assets collected using standardized protocols. These datasets should include comprehensive patient and biomarker information that is curated, validated, and interoperable across systems. Equally critical is ensuring that patient samples are obtained under informed consent that explicitly covers data collection, usage, and protection measures.
Beyond consent, safeguards must be in place to guarantee data privacy, confidentiality, and compliance with evolving regulatory standards such as GDPR. A transparent consent process not only builds trust but also empowers patients, enabling them to realize the substantial benefits of AI-driven technologies in improving diagnosis, treatment personalization, and overall health outcomes.
Vera Pomerantseva, Director of Product Management, RBQM, eClinical Solutions
LinkedIn: Vera Pomerantseva
AI Adoption Will Follow the Path of Risk Management We Saw 10 Years Ago: “Those tracking the slow pace of AI adoption in pharma should remember that any innovation in this space takes considerable time. The rollout of RBQM after 2013 illustrates this, as the conservative nature of the industry required both validation of new technology and a mindset shift. Teams had to evolve from a checklist mentality to understanding of what is critical, and makes sense, to adopt this new approach. AI presents a similar adoption challenge, requiring industry professionals to trust and apply its insights thoughtfully while challenging traditional approaches.
AI has the power to reduce manual effort, freeing data teams to focus on critical decisions and solving new challenges, such as integrating patient insight, use of RWE, flexible study design.. In 2026, I expect the industry to leverage AI to deliver function centric agentic AI to enhance operational efficiency.
Raviv Pryluk, PhD, CEO and co-founder, PhaseV
LinkedIn: Raviv Pryluk
The ‘AI experiment’ is over. Starting in 2026, we won’t just see AI making clinical trials better, we will see AI operating at full, unprecedented scale to cut years off the timeline for new medicines. The real future of clinical development is not just about finding new drugs, it’s using AI and integrated data platforms to deliver them to patients faster than ever before.
Katrina Rice, Chief Delivery Officer Biometrics Services, eClinical Solutions
LinkedIn: Katrina Rice, MS
AI: The Game-Changer for Clinical Trials in 2026
Last year, the only prediction everyone got right was the promise AI held for the life science industry. In 2025, we saw AI go from buzzword to reality, with companies embracing it, responsibly and cautiously, into their existing workflows and products. Next year, AI will touch every aspect and speed up the entire clinical trial lifecycle. From the beginning stages of site selection and patient recruitment, AI will narrow down both based on criteria, location, and target population, speeding up an inherently slow and time-consuming process. In the middle stages, AI will lighten operational burdens by sorting through the influx of data points and identifying any anomalies by pairing technology with risk-based quality management. Finally, at the end stages, it will streamline regulatory submissions to bring drugs to patients faster. Ultimately, AI will transform the entire trial lifecycle, improving both the speed and quality at every single stage.
Scott R. Schell, MD, PhD, MBA, Chief Medical Officer, Cognizant
LinkedIn: Scott Schell
Generative systems now draft clinical protocols, summarize literature, and flag pharmacovigilance signals. These tools accelerate research and operational efficiency, returning time and morale to scientists and clinicians alike.
Rafael Sidi, Senior Vice President & General Manager of Health Research, Wolters Kluwer Health
LinkedIn: Rafael Sidi
The future of health research isn’t just about more data it’s about turning knowledge into impact faster than ever before. In 2026, AI will help us move beyond searching and reading to truly understanding and applying insights in real time. Imagine a world where clinicians don’t have to wait months or years for guidelines to catch up, because AI is continuously synthesizing global evidence and surfacing what matters most. That’s not just efficiency it’s better care, everywhere. AI is rapidly becoming an essential partner for researchers, helping them find, summarize, and synthesize the latest evidence and literature. It’s transforming how journals are published, peer-reviewed, and consumed making scholarly content more dynamic, accessible, and personalized. AI will also play a pivotal role in upholding research integrity by helping make the peer review process more open, efficient, and trustworthy, automating corrections and retractions, and ensuring that the most current, high-quality evidence is always available. Rather than replacing human judgment, AI will strengthen it, creating a future where evidence-based medicine is continuously informed by the latest science delivered faster, smarter, and with greater impact. The opportunity ahead is extraordinary. By leading with purpose, responsibility, and a commitment to innovation, we can shape a future where technology and human expertise work together to advance health research and improve lives.
Mike Sitzman, Partner, McDermott Will & Schulte
LinkedIn: Michael Sitzman
With AI permeating all aspects of our daily lives, AI will likely become more prevalent as a research and analytical tool in the field of bio/pharmaceuticals. Innovations will likely be identified and pursued based on generative AI steering scientists and researchers with predictions on how certain compounds may behave in the body and how certain antibodies may be selected for purposes of achieving a biological result. For many years we have discussed the concept of tailored medicine; with the increased growth and reliability of generative AI, we will get closer and closer to formulating medicines and treatments that are truly tailored to a specific disease state and a specific patient.
While the foregoing advancement will be hugely important and beneficial in the world of healthcare, they pose significant problems for patenting new innovations and new discoveries. If generative AI is responsible for identifying new compounds and/or antibodies, can the result be patented? Under the Patent Act, an AI engine cannot be a “inventor.” Moreover, if the new discovery is based on an engine that has been educated with all of the available prior art, published knowledge and resources, then the outcome may not be truly novel and may not constitute patentable subject matter. If patent protection is not available, will bio-pharmaceutical companies turn away from generative AI to formulate the most effective and important new compound? Or – as I predict – will it be time for a new form of sui generis IP protection that balances the benefits of using AI while rewarding companies with a limited period of exclusivity.
I also believe that in 2026 we will see a decrease in the number of Hatch-Waxman and BPCIA litigation. Since 1984, Brands and Generics have been battling each other in court over bio/pharmaceutical patents with mixed results. And while both parties have become very sophisticated in litigating such patents, there is too much uncertainty about how most cases will come out. Rather than spending millions of dollars to litigate these cases, I predict that there will be more and more pressure on settling these disputes early and often. The FTC has provided some relatively clear guidance on how to settle such cases and the IRA has placed additional pressure on drug pricing. In view of these variables, I believe that the number of Hatch-Waxman and BPCIA cases will drop and that settlements with agreed-upon launch dates will be increase.
Erik Terjesen, Managing Director, Silicon Foundry
LinkedIn: Erik Terjesen
Precision Medicine: Advances in understanding human biology (genetic history, biomarker data) combined with AI will enable more precise and personalized treatments. Companion diagnostics will be increasingly adopted as part of this process.
Greg Tietjen, CEO, Revalia Bio
LinkedIn: Greg Tietjen
Human data-trained models: AI models, built exclusively on high-quality human datasets, will outperform legacy preclinical models by more accurately predicting clinical efficacy and toxicity outcomes.
Dynamic predictive pipelines: Predictive algorithms will integrate multi-omic, imaging, and physiological human data to forecast trial outcomes and patient response trajectories before enrollment.
Rodd Turnquist, Sales Manager – OEM Division, Watson-Marlow Fluid Technology Solutions
LinkedIn: Rodd Turnquist
Anticipated FDA cuts and slower approval timelines will push medical device manufacturers to rely on existing approvals to speed market access. Companies will increasingly turn to streamlined pathways like the FDA’s 510(k) process, favoring devices built on established, validated technologies. The shift will shine a light on suppliers with strong compliance track records that can offer fully tested, quality-assured products that reduce risk and accelerate time-to-market.