AI Disruption, Consumer-Driven Healthcare, & FDA Innovations: Key Trends for Medtech in 2026
Adam Hesse, CEO of Full Spectrum, sees three currents reworking medtech: AI in FDA workflows, the push to consumer-direct care, and the hard parts of deploying remote monitoring outside clinical channels. One theme cuts through them all: AI will be the most disruptive force in 2026. The upside is real, but so are the risks without tight human oversight.
At a Glance
- AI in FDA submissions could shorten review cycles, with smaller companies most motivated to lead.
- Consumer-driven healthcare deepens patient involvement but raises education, risk, and compliance hurdles.
- AI implementation will be the biggest disruptor in 2026, touching R&D, regulatory, and delivery.
AI in FDA Submissions: Momentum and Practical Next Steps
FDA rolled out an AI-related program in June 2025, but little has been shared publicly about throughput or outcomes so far, according to Hesse. For it to matter, total review time must drop. Expect smaller organizations to push hardest, because a faster path can be the difference between surviving and stalling. Over time, Hesse expects pre-screening capabilities that give submitters rapid feedback loops.
For context on FDA's broader approach to AI in devices, see FDA's AI/ML SaMD resources here. If you pilot AI-assisted submission prep, start with low-risk sections, log every AI output, and require human sign-off with clear rationale fields.
AI "Over-Correction": Why Novel Ideas Get Penalized
Hesse points to a pattern seen in academic grading: AI tends to judge based on similarity to prior work. That means novel approaches can get flagged or scored harshly simply for being different. In medtech, that can bury good ideas. Keep a human reviewer in the loop to assess whether AI findings are relevant or just conformity bias.
Consumer-Direct Care and GLP-1s: A New Patient Dialogue
Consumer-driven healthcare lets patients take the wheel. Hesse expects more engaged patients and deeper conversations with clinicians, not fewer. Digital tools can route people to the right therapy, diagnostic, or service based on history and symptoms-often without a clinic visit. For background on GLP-1 therapies, see the NIDDK overview here.
Direct-to-Consumer Remote Monitoring: Risks You Must Manage
Matching the right person to the right tool is hard enough inside a clinic. Go direct to consumer and the burden shifts to your product experience. Hesse flags two pressure points: patient education and the risk of people selecting the wrong pathway. Teams should invest in plain-language onboarding, eligibility gates, and crisp escalation triggers to clinicians when risks or exclusions appear.
HSAs: Helpful Enabler, Not the Driver
HSAs support the trend, but they won't drive it, says Hesse. Policy changes that expand access and contribution limits would help. The real driver will be companies that deliver clear outcomes at a cost people accept-and make it easy for patients to act on their own.
The Big Disruptor for 2026: AI Implementation
AI will touch everything-from drafting product requirements and submission content to assisting FDA reviews. Every company needs a position on what AI will do, where it won't be used, and how it will be controlled. Hesse expects some teams to deploy AI the wrong way or with weak guardrails.
Keeping a human in the loop is the baseline, but it's not foolproof. As people grow comfortable, they can mentally check out. Some have suggested injecting deliberate AI "checks" to keep humans engaged; that may preserve attention but undercuts efficiency. A better path: force periodic human justification, spot audits, and performance metrics on both the AI and the reviewer.
What Healthcare Leaders Should Do Now
- Set an AI policy: approved use cases, data sources, security, validation, and audit trails.
- Start small: use AI for literature summaries, requirement drafts, and submission formatting, with mandatory human edits.
- Design human-in-the-loop controls that measure attention (justification prompts, randomized spot-checks) instead of adding busywork.
- For D2C RPM, build eligibility gates, clear exclusions, and "route to clinician" triggers into the product flow.
- Quantify risk of misrouting and document mitigations before launch; involve quality, regulatory, and clinical early.
- Treat HSAs as a boost, not a crutch; your unit economics must stand on their own.
If your team needs to skill up on practical AI use across roles, explore curated options here.
Your membership also unlocks:
