Transforming advanced healthcare and outcomes with AI in life sciences.

Scientific discovery has always been a balance between creative thinking and logical proof.

Now, artificial intelligence (AI) is helping researchers unlock insights from massive amounts of data, such as patient records. This has opened up a world of possibilities for AI in life sciences. These developments are creating opportunities for more precise, personalized medicine, improving clinical trials and medical imaging, and optimizing healthcare operations.

This article takes a closer look at how AI is making life sciences more efficient, precise, and patient-focused.

Pharmaceutical companies, biotechnology startups, and contract research organizations are using AI to speed up the search for new drug candidates. Researchers work to identify chemical compounds or biological agents that can safely and effectively treat diseases — a process that traditionally takes years.

AI is transforming drug discovery in several ways.

Pharma AI solutions are helping researchers identify disease-causing targets, such as proteins or genes linked to an illness, with greater speed and accuracy. Advanced algorithms can analyze massive biological datasets, drastically reducing the time needed to search for viable treatment options. Generative AI takes this even further, combining structured and unstructured data — like research papers, clinical images, and molecular structures — to detect patterns and suggest novel compounds that could fight disease.

The medical technology (MedTech) sector is also closely linked to drug discovery, especially now that some medical devices include AI capabilities. While well-known medical devices like pacemakers and imaging instruments help treat diseases and illnesses, other devices can accelerate drug discovery through lab automation and enhanced diagnostics.

AI is also making precision medicine more effective by helping doctors tailor treatments to each patient's unique genetic makeup. Just a few years ago, it took nine weeks to analyze a single genome. Today, researchers can process thousands of genomes in just a few hours, according to the Association of Cancer Care Centers — a breakthrough that's paving the way for faster, more personalized treatments.

Clinical trials are a vital step in bringing new treatments to patients, but the process is often slow and complex. Each trial goes through multiple phases to confirm a treatment's safety and effectiveness, and even small efficiencies can make a big difference in how quickly life-changing therapies reach those who need them. AI is helping streamline every stage of the process, from site selection and protocol development to benefits verification and end-to-end trial design.

One of the biggest challenges in clinical trials is finding the right locations. Agentic AI can analyze vast datasets to pinpoint the optimal trial sites — those with the best chances of success based on past performance, patient demographics, and logistical factors.

Another major hurdle is patient recruitment, which is often time-consuming and inefficient. AI can help by automatically matching eligible participants to relevant trials based on study criteria, patient history, and risk factors. Instead of manually screening thousands of applicants, AI can quickly sort through electronic health records and claims data to identify qualified candidates in a fraction of the time.

Modern tools are making this process even smoother. Generative AI helps reduce the burden of manual screening and assessments by automatically reviewing and matching patients to trials. By consolidating data from multiple sources, AI helps life sciences teams quickly generate targeted participant segments, saving time and ensuring trials are filled with the most suitable candidates.

Want to learn more about AI's role in clinical trials? Check out this webinar for insights on how AI is transforming trial management and patient recruitment.

Personalized medicine is transforming the life sciences industry, offering more precise diagnoses and tailored treatments. It now accounts for over a third of FDA-approved new molecular entities and that number continues to grow. But with eight billion people worldwide, each carrying a unique combination of 20,000–25,000 genes , delivering truly individualized care is a massive challenge.

AI is making personalized medicine more accessible by helping doctors analyze complex genetic data, refine diagnoses, and tailor treatments to each patient's unique needs. It also plays a role in patient relationship management, ensuring more informed and seamless care.

According to Cureus Journal of Medical Science , AI is significantly accelerating genomic data interpretation, allowing doctors to deliver faster, more accurate diagnoses and treatment plans for genetic conditions. Medicine is no longer a one-size-fits-all approach. AI makes it possible to customize care at an unprecedented scale.

Healthcare leaders are taking notice. A Forrester study found that 76% of healthcare decision-makers see AI as a key driver for creating more personalized patient interactions, whether for individuals or entire populations.

Deep learning is making medical imaging more accurate and efficient. In a recent webinar, experts discussed Medtronic's AI-powered endoscopy, which uses real-time imaging and deep learning to help doctors spot pre-cancerous polyps — a key step in preventing colon cancer.

AI is also simplifying radiology workflows , automatically sorting images, assisting with diagnoses, and even predicting patient outcomes. These tools are reducing the burden on radiologists while improving speed and accuracy in detecting diseases.

Managing the life sciences supply chain is complex, but AI is making it more efficient.

AI agents — software programs that handle tasks automatically — are already streamlining operations. AI-powered inventory agents can save time for sales reps by optimizing stock levels while predictive models help life sciences teams stay connected with healthcare providers.

The industry is also taking a page from manufacturing, using AI for predictive maintenance. If critical medical devices like imaging instruments break down unexpectedly, it can cause major disruptions. AI analyzes sensor data to detect early warning signs so facilities can schedule maintenance before a problem arises.

AI in life sciences relies on vast amounts of data, including personally identifiable information (PII) and protected health information (PHI). Keeping that data secure isn't just a legal requirement. It's critical for protecting patients and proprietary research.

AI's need for high-quality datasets creates a risk of exposing sensitive information, but it can also help solve the problem. Salesforce Life Sciences CRM provides a trusted, connected platform that includes AI-driven tools to help reduce the potential for manual error.

Bias is another challenge. If an AI model is trained on data from a specific group — like a research hospital in a major city — it may not perform well for other populations. Life science organizations can reduce bias by reviewing and adjusting training data.

AI opens new possibilities in life sciences, but ethical and regulatory safeguards matter just as much as innovation.

One of AI's biggest advantages is that the more it's used, the smarter it gets.

AI is already transforming the life sciences industry, but this is just the beginning. Generative AI has the potential to reshape drug discovery and design, helping researchers develop complex molecules faster than ever. Synthetic biology is another frontier, where AI is unlocking the potential of dynamic, intricate proteins that could lead to groundbreaking treatments. Agentic AI could further streamline operations, reducing the time and resources spent on manual tasks like benefits verification.

Wearables are also evolving. Some smartwatches detect irregular heart rhythms. Future AI-powered wearables could track biomarkers that signal disease before symptoms appear. AI is also advancing genetic research and healthcare technology, which helps researchers process massive datasets and speed up discoveries in generative biology.

AI is cutting down the time it takes to develop drugs, decode genetic data, and diagnose diseases. The companies making the leap now will decide what medicine looks like in the decades ahead.

So many industries are embracing the latest technologies, and the excitement is building.

While there is still healthy skepticism, there are so many opportunities for AI in life sciences. And while, like in many other industries, the efficiencies and capabilities that AI unlocks offer major benefits to the bottom line, in life sciences, the stakes are even higher. The latest advancements can help life sciences professionals develop new treatments faster, ultimately leading to improved healthcare outcomes.