Diagnosing the Future: AI Transforming Healthcare Systems
Detailed Summary
John Schaeffler opened the discussion by asking the panel about the regulatory status of predictive AI tools (e.g., cancer‑presence vs. cancer‑risk models). He noted four regulatory “models” – EU, UK (England), India, and the United States – and asked how each influences adoption.
- Key Insight (John) – The regulatory landscape is the primary bottleneck for predictive AI; the “standard‑of‑practice” shift will happen once clear pathways exist.
- Data Point – John referenced the FDA (U.S.) and implied that global harmonisation is still nascent.
2. Clinician Perspective: Over‑ vs. Under‑Estimation
Dr Krithika Rangarajan (referred to as “Kritika”) reflected on the paradox of clinicians both over‑estimating AI’s capabilities (e.g., papers claiming two‑year‑ahead cancer detection) and under‑estimating them (fear of missing a cancer case).
- Under‑estimation – She described a breast‑cancer triage model that could operate without a radiologist, noting the “goosebumps” feeling about population‑scale impact.
- Over‑estimation – She warned about false‑positive cascades and anxiety generated by high‑risk predictions (e.g., a 60 % cancer‑risk output prompting unnecessary biopsies).
Recommendation – Kritika suggested focusing on negative‑predictive value (high confidence in “no cancer”) to expand screening capacity rather than on positive alerts that may generate false alarms.
3. The “Threshold Problem” – Defining Positive vs. Negative
The panel debated how to set actionable probability cut‑offs. A pragmatic proposal emerged:
- >80 % – Treat as positive (intervention).
- <20 % – Treat as negative (reassure).
- 20‑80 % – Default to standard clinical judgment (no AI‑driven decision).
This “uncertainty band” acknowledges AI’s probabilistic nature while preserving clinician autonomy.
4. Investment Lens – From High‑Risk to High‑Reward
Mr Apoorva Patni shifted the conversation to capital‑allocation. He contrasted the “high‑risk, high‑reward” mindset of venture investors with the cautious, outcome‑driven expectations of clinicians.
- Historical Trend – Early health‑tech investments (2016‑17) pre‑AI have yielded solid returns, creating a “vintage” that now favours AI‑centric startups.
- Market Size – 2023‑24 U.S. health‑tech investment reached $15 billion, ~60 % AI‑related.
- Geographical Focus – Western AI firms chase cost‑optimization (ambient clinical intelligence, transcription) and diagnostics. Indian startups gravitate toward distribution, reach, and low‑cost point‑of‑care devices (e.g., $50–100 k imaging units with embedded AI).
Key Insight – Small‑language‑model (SLM) solutions are more attractive in emerging markets because they satisfy data‑sovereignty and energy‑efficiency constraints.
5. Industry Perspectives
5.1 Roche – From Retrospective to Real‑Time Analytics
Adarsh Srivastava explained Roche’s evolution:
- Five‑years‑ago – Analytics were primarily retrospective, siloed, and static.
- Now – With improved data interoperability (EHR/EMR integration) and generative AI, Roche can deliver end‑to‑end insights spanning pre‑diagnostic, diagnostic, and post‑diagnostic phases.
He highlighted two non‑LLM use‑cases:
- Instrument‑level error detection – Early warning if a diagnostic device is malfunctioning.
- Sample‑track integrity – Continuous monitoring of specimen temperature and handling to reduce wastage.
These require high‑quality, time‑series data, not just large language models.
5.2 GE HealthCare Foundation – Mission‑Driven AI
John Schaeffler described the foundation’s recent launch:
- Mission – Maternal health and health‑worker training, deliberately separated from GE’s commercial product lines.
- Metrics – Building a measurement framework to assess impact; AI will be leveraged primarily for workflow optimisation (e.g., triage, training simulations).
On the commercial side, John noted GE’s acquisition strategy (e.g., MIMS, BK Medical) now targets software‑enabled AI capabilities—the company no longer competes on hardware alone.
5.3 Carpl.ai – Clinical‑Workflow Automation
Vidur Mahajan enumerated six flagship AI use‑cases across the radiology continuum:
| Use‑case | Clinical impact |
|---|---|
| Stroke detection (CT) | Immediate neuro‑team alerts, reduced door‑to‑needle time |
| Lung‑cancer screening (CT) | Faster nodule identification, reduced miss rate |
| TB screening (X‑ray) | Nationwide AI‑only triage; ~5 000 daily screenings in India |
| Mammography (Singapore) | AI‑augmented reads marketed directly to consumers |
| Cardiac calcium scoring (CT) | Automated risk stratification |
| Spine MRI reporting | Auto‑generated structured reports |
He emphasized security as the greatest deployment hurdle (risk of ransomware, data breaches).
5.4 AIIMS – Public‑Health‑Centric AI (AIMS)
Kritika gave a three‑part overview of the AI‑Enabled Imaging and Management System (AIMS):
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Deployed/Clinical‑Trial Tools –
- MAMO (AI‑triage for mammograms) – currently in multicenter trial.
- Chest‑X‑ray AI – preliminary reads auto‑generated.
- Diabetic‑retinopathy AI – low‑cost handheld camera integration; CDSCO approval pending.
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Workflow‑Focused Innovations –
- Patient‑summarisation before clinician encounter.
- Cough‑analysis AI for preliminary respiratory assessment.
- Clinical‑Decision‑Support Systems (CDSS) for chronic disease pathways (diabetes, hypertension).
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Research & Discovery – Large questionnaire‑driven study (≈80 questions) to uncover emerging risk‑factor patterns in Indian breast‑cancer incidence (e.g., lifestyle, environmental factors).
Kritika stressed that LLMs cannot replace high‑fidelity clinical data; garbage‑in yields garbage‑out.
6. Government Perspective – Building an “AI‑Ready” Health System
Shri G. Veerapandian (Mission Director, NHM, Andhra Pradesh) presented a candid appraisal of the state’s digital health journey:
- Current Gaps – Fragmented data flow across primary, secondary, tertiary levels; lack of interoperability even within subsystems (e.g., women’s health vs. child health).
- Standardisation – Recent Indian‑government standards exist but are not fully mature or consistently applied.
- Workforce Readiness – Health‑personnel training on AI remains inadequate; decisions still rely heavily on human judgment.
He described a “digital‑burden paradox”: digitisation increased data collection but did not translate into actionable insight, leading to perfunctory data entry for performance dashboards.
Key Initiative – “Sanjivani”
- Vision: Create a digital health profile for every citizen in Andhra Pradesh.
- Recent collaboration with the Bill & Melinda Gates Foundation to amplify AI‑driven public‑health pilots.
Methek Hackathon – 192 applications, 20 shortlisted use‑cases now in sandbox testing (both physical and digital).
Takeaway – AI is seen as a fabric to overlay existing digital infrastructure, not as a standalone solution.
7. Patient‑Centred Viewpoint
Apoorva Patni addressed the patient‑side demand for AI:
- Most patients already self‑triage via internet searches (e.g., “Google”).
- Younger doctors welcome AI for administrative relief; senior clinicians worry about liability and accountability.
- In high‑stakes contexts (e.g., IVF), patients still place trust primarily in the human physician; AI is viewed as a supportive adjunct, not a decision‑maker.
A noteworthy observation: some clinicians may use AI to claim plausible deniability (“I acted on AI recommendation”), potentially reshaping liability frameworks.
8. Open Questions & Debates
| Issue | Position(s) |
|---|---|
| Regulatory clarity for predictive risk models | John: Need harmonised standards; Kritika: Fear of false positives vs. population‑scale benefit. |
| LLM vs. domain‑specific AI | Vidur: LLMs good for summarisation; not a substitute for high‑quality, structured clinical data. |
| Investment focus – diagnostics vs. devices | Apoorva: Diagnostics (AI‑enabled imaging) attracts most capital; devices with SLMs open new markets. |
| Liability & “human‑in‑the‑loop” | Apoorva & Others: Consensus that a human must remain final arbiter; future legal frameworks required. |
| Data sovereignty & on‑prem vs. cloud | Kritika: SLMs enable on‑prem deployment, respecting national data‑privacy mandates. |
9. Q&A Highlights
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Audience Query (to John) – “What concrete AI initiatives is GE driving?”
John reiterated GE’s stroke‑alert AI, lung‑cancer screening, and AI‑only TB screening platforms, stressing the need for robust IT security. -
Audience Query (to Veerapandian) – “How are you handling the digital‑burden paradox?”
Veerapandian described process‑re‑engineering to shift from data entry for compliance to data‑driven decision support, leveraging AI to turn existing digitised records into actionable insights. -
Audience Query (to Vidur) – “Can LLMs replace traditional AI in clinical diagnostics?”
Vidur answered that LLMs complement, not replace, structured models; they excel at document summarisation but cannot yet generate reliable diagnostic predictions without curated datasets.
10. Closing Remarks & Session Wrap‑Up
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John Schaeffler (via brief concluding remarks) highlighted the global interest in Indian AI health‑tech (e.g., Google’s large exhibition presence) and invoked the popular phrase “Apna time aayega” to signal India’s emerging leadership.
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Moderator thanked panelists, invited the audience for a group photograph, and formally closed the session.
Key Takeaways
- Regulatory Harmonisation is Critical – Predictive AI adoption hinges on clear, globally aligned pathways for risk‑based models.
- Threshold Setting Balances Safety & Scale – Defining explicit probability bands (e.g., >80 % positive, <20 % negative) mitigates false‑alarm anxiety while enabling mass screening.
- Negative‑Predictive Focus Expands Reach – High confidence that a patient does not have disease can safely free radiologists to concentrate on positives.
- Small‑Language‑Model (SLM) Solutions Suit Emerging Markets – On‑prem, low‑resource AI respects data‑sovereignty and energy constraints, making point‑of‑care devices viable.
- Investment Momentum Favors AI‑Enabled Diagnostics & Workflow Automation – 2023‑24 health‑tech funding reached $15 bn, with ~60 % dedicated to AI.
- Clinical‑Workflow Integration Requires High‑Quality, Structured Data – LLMs aid summarisation; domain‑specific models still demand curated datasets and rigorous validation.
- Public‑Sector Digital Journey Is a Foundation, Not a Destination – Andhra Pradesh’s “digital‑burden paradox” illustrates that digitisation alone does not yield insight; AI must be the analytical layer atop existing data.
- Human‑in‑the‑Loop Remains Non‑Negotiable – Both clinicians and patients view AI as an assistive tool; liability frameworks must evolve to reflect shared decision‑making.
- Collaborative Ecosystem Needed – Successful scaling demands coordination among regulators, academia, industry, investors, and government health programmes.
- India’s AI Health‑Tech Momentum Is Evident – Robust startup activity, multinational interest, and emerging public‑private pilots signal that “Apna time” for AI‑driven healthcare in India has arrived.
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