Introduction For most of the modern pharmaceutical era, biotechnology was a healthcare industry; it is no longer treated like one. In a 2025 Executive Order1, the White House mandated that federally funded biological research must benefit Americans ‘without jeopardising’ national security, economic strength, or prosperity. The economic stakes help explain the shift: while the biotechnology…

Deep learning models hold the potential to be integrated into clinical workflows, providing clinicians with a useful tool to help diagnose and plan the most appropriate action for their patients. However, for such models to be effective, generalisable and ethically promising, they must train on extensive datasets. These datasets are difficult to acquire in the…

Conventional therapies for spinal cord injury, such as rehabilitation and epidural electrical stimulation, have had limited success in restoring true voluntary walking. Brain-spine interfaces offer a promising solution, enabling voluntary control of movement by linking cortical signals from the sensorimotor cortex to targeted spinal cord stimulation. This article examines how they work, their clinical outcomes…

In this interview, Dr. Christian Rohlff, CEO of Oxford BioTherapeutics, discusses how shifting from genomics to AI-driven proteomics can break the biotech industry’s echo chamber of redundant drug development. He further outlines the strategic partnerships and machine learning innovations required to reach the significant portion of oncology patients who are underserved by current therapeutics.

Biohacking spans everything from harmless self-tracking to irreversible biological interventions, which is why it can look like either sensible self-care or reckless experimentation depending on where you draw the line. The biohacker’s dilemma is that the same user-led drive can expose unmet needs and accelerate clinically valuable innovation, but without guardrails, it can also normalise…

Many modern MedTech innovations rely on deep learning systems, yet their internal decision-making processes remain largely opaque. This raises an important question: when does accuracy alone suffice, and when does medicine require deeper understanding?