Forums – Page 4 – AIiHC ORG

Forum Description

Purpose: Connecting experienced professionals with those seeking guidance and career development in the AI in healthcare field.

Description: This subgroup facilitates formal and informal mentorship relationships, allowing members to seek advice, share experiences, and guide others in their professional journey within healthcare AI.

Intended Use: For connecting mentors and mentees, sharing best practices for mentorship, and fostering career growth and knowledge transfer within the community.

Limitations: Focus is on *mentorship and career guidance*. Not for specific project work or technical tutoring.

Key Activities: Posting mentorship requests/offers, sharing tips for effective mentorship relationships, organizing virtual mentorship events.

Potential Users: All user roles (both mentors and mentees)

Possible Discussions: “Seeking a mentor for a career in healthcare AI ethics,” “Tips for being an effective mentor in a rapidly evolving field,” “My experience as a mentee in healthcare AI.”

Forum Description

Purpose: Provide a non-commercial space for companies developing AI solutions for healthcare delivery to exchange insights, understand clinical needs, and engage in constructive dialogue with clinicians, leaders, and researchers.

Description : This group brings together software companies building AI tools for clinical care and hospital operations. It includes vendors working on diagnostics, workflow automation, triage tools, patient engagement apps, and administrative AI. The group allows them to discuss real-world challenges (validation, integration, ethics) without promoting products.

Intended Use: Share implementation lessons (e.g., deployment hurdles, usability issues).Explore regulatory and ethical requirements across regions.Receive feedback from healthcare professionals about usability, workflow fit, and trust.Collaborate with researchers on validation studies and evidence-building.

Limitations: Strictly no marketing, demos, or sales pitches.No product names in post titles (allowed in context if relevant to a regulatory/evidence question).Must remain knowledge-sharing only, not lead generation.Moderated closely to maintain neutrality.

Key Activities: Posting anonymized case lessons (“What we learned from integrating an AI chest X-ray tool in 2 hospitals”).Sharing non-branded validation evidence formats(sensitivity/specificity, workflow fit).Discussing integration hurdles (API, PACS, EHR).Engaging in Q&A with clinicians to better understand needs.

Potential Users: AI vendor teams (founders, product managers, clinical scientists, regulatory leads).Clinicians & healthcare leaders looking to understand vendor perspectives.Researchers exploring collaboration opportunities with vendors.Policy/ethics experts monitoring industry practices.

Possible Discussions:“How do hospitals evaluate usability of AI triage systems before procurement?”.“What validation formats are most accepted by radiology departments?”.“Lessons learned from submitting an AI tool for FDA clearance.”“How should vendors communicate false-positive rates to clinicians?”

Other Important Notes:Vendors joining this group must agree to the AIiHC Non-Commercial Pledge.

Pinned posts will include:Posting Guidelines (knowledge-sharing only, no promotion).Evidence Template (how to share case results responsibly).Moderators should actively encourage clinician–vendor dialogue, but redirect overt sales talk to external forums.

Forum Description

Purpose:
To bring together companies that provide the technical backbone for AI in healthcare — from marketplaces hosting multiple apps, to MLOps platforms, to interoperability providers.
This group enables discussion on scaling AI adoption, secure infrastructure, and seamless integration into hospital IT systems.

Description:
This group includes AI platform vendors (e.g., Blackford, Philips AI Manager, GE Edison, Siemens teamplay, deepc, Incepto), as well as infrastructure partners like cloud providers, data pipeline enablers, and standards bodies.They do not focus on building one app, but on enabling hospitals to run multiple AI solutions efficiently and safely.

Intended Use:
Share lessons on multi-vendor orchestration (managing 5+ AI apps in one hospital).Discuss integration with PACS/EHR/HIS and interoperability standards.Explore deployment strategies (cloud vs on-prem vs hybrid).Provide insights into performance monitoring (uptime, drift detection, retraining).

Limitations:
Strict no commercial promotions or “platform demos.”No sales comparisons (“why our platform is better than X”).Proprietary code and pricing cannot be shared.Discussions must remain solution-focused, not competitive marketing.

Key Activities:
Posting implementation case studies (without product branding).Sharing best practices for hospital orchestration.Co-developing standards discussions with IT professionals.Hosting multi-vendor Q&A with clinicians and administrators.

Potential Users:
AI platform vendors (Blackford, deepc, Incepto, GE Edison, Philips, Siemens teamplay).Cloud infrastructure providers (AWS, Azure, GCP — healthcare-specific divisions).Hospital IT & integration specialists.Regulatory experts exploring how platforms fit into MDR/EU AI Act.Researchers working on federated learning and interoperability.

Possible Discussions:
“How can hospitals evaluate orchestration platforms fairly?”.“What’s the biggest challenge in integrating multiple AI apps into PACS/EHR workflows?”.“How should AI marketplaces report app performance post-deployment?”.“Cloud vs on-prem: what’s the safer bet under EU AI Act?”.“What’s the role of IHE AIR and FHIR standards in multi-vendor environments?”

Other Important Notes:
This group is very strategic — many hospitals are confused by the number of platforms emerging.A well-structured forum here can make AIiHC a neutral comparison space (without naming/shaming vendors).It will naturally attract vendors and hospitals looking for integration insights.Strong crossover with IT & Infrastructure Professionals in Pillar 1.4 and Standards & Best Practices in Pillar 3.5.

Forum Description

Purpose:
To provide a space for neutral implementation partners — companies that don’t just sell AI tools, but help hospitals evaluate, procure, deploy, validate, and sustain AI responsibly.

Description:
This group is for consultancies, service providers, and independent validation organizations that guide hospitals through the AI adoption journey.
Unlike product vendors, these companies focus on process expertise: strategy, workflow adaptation, evidence-building, and change management.
Examples: Romion Health, Deloitte Health AI practices, Accenture Health, MedPerf collaborations, CRO validation partners.

Intended Use:
Share lessons learned from supporting hospitals with AI adoption.Provide frameworks for procurement, governance, and evaluation.Discuss training and workforce readiness programs.Collaborate with vendors and hospitals to ensure evidence-based implementation.

Limitations:
Strict no product promotion (they must avoid acting as resellers).Cannot endorse or “push” specific vendor products.Proprietary client details must remain confidential — only generalized lessons allowed.Focus must remain on responsible process support, not consulting sales pitches.

Key Activities:
Posting neutral case lessons (e.g., “Challenges we saw when a mid-sized hospital adopted 3 AI tools at once”).Sharing procurement templates and checklists.Hosting Q&A with clinicians about implementation hurdles.Bringing in multidisciplinary best practices (governance, change management, IT readiness).

Potential Users:
Implementation consultancies (Romion Health, Accenture, Deloitte, PwC Health AI teams).Independent validation networks (Health AI Register, MedPerf/MLCommons).Hospital project managers working with external implementation partners.Researchers interested in studying adoption pathways.Clinicians who want to learn how hospitals structure implementation programs.

Possible Discussions:
“What governance model works best for AI steering committees in hospitals?”.“How do you evaluate multiple vendors fairly in procurement?”.“Lessons learned from training radiologists and nurses to trust AI outputs.”.“How can independent registries like Health AI Register support transparency?”.“What role should consultancies play in post-market surveillance?”

Other Important Notes:
This group is crucial for bridging the gap between theory and practice — making AI adoption safe, responsible, and efficient.It connects strongly to Pillar 2 (AI Adoption Journey) since these partners often help hospitals through those exact steps.It also links to Pillar 3 (Foundational Principles) (ethics, data governance, regulation).Many hospitals may rely on these partners — so having them here adds real-world expertise for clinicians and administrators in AIiHC.

Forum Description

Purpose:
To provide a neutral collaboration space for medical device companies, pharma R&D teams, and digital health innovators who embed AI into hardware or drug development processes.

Description:
Unlike pure software vendors, this group includes companies whose AI is packaged inside medical devices, diagnostics, wearables, or drug discovery platforms.
Their focus is on hardware-AI convergence and AI-driven biomedical innovation.
Examples:
MedTech AI → Siemens Healthineers, Philips, Canon, GE Healthcare (smart imaging devices, AI in scanners).Pharma AI → BenevolentAI, Exscientia, Pfizer (drug discovery & trial optimization).Wearables & Remote Devices → Apple, Fitbit, Withings, AliveCor, Oura (biosensor-driven AI health tracking).

Intended Use:
Share insights on regulatory challenges for AI-embedded devices.Discuss how AI hardware integrates into clinical workflows.Explore AI in drug discovery pipelines (trial design, molecule screening).Exchange lessons on patient adoption of wearables and remote devices.

Limitations:
No promotion of specific devices, drugs, or commercial offerings.No direct-to-patient marketing (especially for wearables).Proprietary R&D pipelines cannot be disclosed — only principles and anonymized lessons.Pharma discussions must remain at process level, not tied to specific products.

Key Activities:
Sharing case lessons (e.g., “Challenges in validating AI-embedded CT scanners in Europe”).Discussing wearable adoption barriers (trust, data privacy, accuracy).Posting trial design frameworks for AI in clinical studies.Hosting cross-industry conversations between pharma, medtech, and clinicians.

Potential Users:
MedTech companies embedding AI in diagnostic & monitoring devices.Pharma companies using AI in R&D and clinical trials.Wearable/device innovators building consumer or clinical-grade devices.Clinicians & hospital leaders evaluating device adoption.Researchers studying AI in drug discovery or biomedical devices.

Possible Discussions:
“How do regulators classify AI-embedded imaging scanners under MDR?”.“What lessons can pharma AI learn from medical device validation?”.“How do you validate AI-driven wearables for chronic disease management?”.“What is the role of AI in optimizing multi-site drug trials?”.“How do patients perceive trustworthiness in wearable health AI?”

Other Important Notes:
This group brings hardware players (medtech, wearables) and drug discovery innovators into AIiHC, broadening beyond imaging AI.It links strongly with Pillar 4 (AI in Clinical & Operational Domains), especially radiology, cardiology, and pathology.It is attractive to cross-industry partnerships (pharma + medtech + AI startups).AIiHC moderators must monitor carefully to prevent product-centric marketing posts.

1. Stakeholder Connect

2. AI Adoption cycle

3. Ethics & Governances

4. Clinical Domains

5. Academics

6. Industry Exchange

7. Emerging & Future Trends

All Discussions