We explore ten newly developed reporting guidelines for AI research in medicine, their potential impact on the field, and gaps between their intended and actual use by researchers and journals.

We explore why data engineering and clinical integration are major bottlenecks towards bringing AI tools to routine care, and identify other areas beyond the clinic more suited for AI development and deployment.

We explore the AI in pathology landscape through a group of 16 startups, AI applications beyond image interpretation, and how digitization and AI can mature alongside one another to produce tightly integrated solutions.

We explore a new emerging type of healthcare startups with AI offerings across medical specialities - a "multi-speciality AI vendor", and how ideas for clinical AI-centric products can be validated without AI.