In 2025, artificial intelligence (AI) emerged as a critical component in life sciences, enhancing human insight rather than replacing it. AI and automation have become essential throughout the brand lifecycle, facilitating innovation with unprecedented speed and confidence. As researchers harnessed advanced computational power, they utilized AI to enhance scientific decision-making and optimize research and development (R&D) processes.

Key applications of AI included automated trial designs and sophisticated model training for patient-matching and feasibility planning. These capabilities streamlined the trial process, enabling faster and more efficient outcomes. Additionally, AI-driven automation transformed evidence generation and market access strategies, allowing companies to navigate global complexities while focusing on strategic decision-making rather than administrative tasks.

AI also revolutionized communication within healthcare. Personalized approaches, once common in retail, were effectively applied to engage patients, caregivers, and healthcare professionals, maintaining relevance while avoiding intrusion. Successful organizations leveraged AI for clearer, more accessible communication, reinforcing the importance of timing and context.

The resurgence of AI-powered search capabilities redefined how healthcare information is accessed and understood. This evolution encourages the creation of content that accommodates both human and machine navigation, thereby enhancing medical information, disease awareness, and clinical education.

While technology enables scale and efficiency, the true differentiator remains human insight. In life sciences, genuine communication reflects the realities of people's experiences and needs. As 2026 approaches, organizations will gain a competitive edge not just by adopting AI, but by operationalizing its potential to drive strategic impact, ultimately benefiting patients and communities at large.

Hello, I'm Dr. Maurie Markman from City of Hope. Today, I want to discuss a fascinating application of artificial intelligence (AI) in enhancing cancer care, as illustrated in the paper “Patient-Centered Research Through Artificial Intelligence to Identify Priorities in Cancer Care,” published in JAMA Oncology.

AI’s integration in healthcare is extensive, particularly in imaging and pathology. However, this study explores a novel application of AI: analyzing de-identified patient portal messages to generate patient-centered research questions. By leveraging AI to sift through 614,464 messages from 25,549 patients—10,665 with breast cancer and 14,884 with skin cancer—researchers aimed to identify meaningful inquiries that reflect patients' priorities and concerns.

Three oncologists independently assessed the AI-generated questions for both meaningfulness and novelty. Remarkably, one-third of the questions were deemed meaningful and novel, while two-thirds showcased innovative ideas relevant to cancer care. Such findings underscore the potential of AI to illuminate new avenues for research.

Examples of generated topics include: interdisciplinary approaches for managing dental care in breast cancer patients, specialized skincare regimens for these patients, preventive dental care efficacy, a digital tool for postsurgical wound care, and a longitudinal study on patient anxiety regarding mole surveillance.

This study exemplifies how AI can bridge the gap between patient concerns and clinical research, paving the way for patient-centered healthcare solutions. I encourage further exploration of AI's capabilities to generate insightful research questions in oncology and beyond. Thank you for your attention.

PostCare AI Pilots Platform to Enhance Efficiency in Small Medical Practices
The Maryland-based healthtech startup leverages AI to streamline medical documentation, helping doctors minimize paperwork and improve patient interactions.

Founded by Surjodeep Sarkar and partners in 2023, PostCare AI aims to tackle the problem of excessive administrative work that consumes over 15 hours a week for physicians. Unlike traditional AI medical scribes focused solely on visit notetaking, this innovative platform provides a pre-charting tool that syncs electronic medical records, such as medical history and lab reports, into a comprehensive patient summary prior to appointments.

Through this pre-visit preparation, healthcare providers benefit from a nuanced patient overview during the consultation, allowing for more focused and attentive care. PostCare AI enhances the documentation process by automatically generating detailed reports that include diagnoses and essential medical billing codes, crucial for preventing costly insurance claim denials and audits. With the industry facing $36 billion in losses due to these coding errors, the platform offers significant financial relief for medical practices.

Currently in beta, PostCare AI collaborates with allergy specialists in the mid-Atlantic region, with plans for expansion into various specialties. By prioritizing small practices over larger hospital systems, PostCare AI is addressing a vital market need. Recently, the company secured a $25,000 grant to further its development and is exploring reasonable pricing structures to ensure affordability for practitioners.

By enabling doctors to focus on patient care rather than paperwork, PostCare AI significantly improves the overall healthcare experience for both providers and patients alike.

In recent years, artificial intelligence (AI) has transformed the analysis of medical images, proving invaluable in the diagnosis and treatment of critical diseases like cancer. Advanced computing power and extensive medical datasets enable AI systems to efficiently process thousands of images, identifying patterns in X-rays, MRIs, and CT scans faster than human experts. According to Onur Asan, an associate professor at Stevens Institute of Technology, “AI does not get tired or lose focus,” providing consistent analyses that support quicker clinical decisions.

However, clinicians often exhibit skepticism towards AI due to the “black box” problem, where the decision-making process of AI is unclear. To address this, Asan and his team, including PhD student Olya Rezaeian, studied 28 oncologists and radiologists analyzing breast cancer images with varying levels of AI-generated explanations. Their findings indicated that while AI improved diagnostic accuracy, increased explainability did not necessarily enhance trust among clinicians. Complex explanations can burden clinicians, diverting them from directly analyzing images and potentially degrading performance.

Asan's research highlights the importance of designing AI systems with a balanced approach to explanations, avoiding excessive cognitive load on users. Furthermore, clinicians must be trained not only to trust AI recommendations but also to maintain critical oversight. As Asan notes, achieving a balance between perceived usefulness and ease of use is crucial for the successful integration of AI tools in clinical practice. With careful system design and comprehensive training, AI can serve as a powerful ally in the medical field, enhancing diagnostic capabilities and improving patient outcomes.