AI for Medical Imaging and Clinical Diagnostics

Abstract

The panel explored how artificial intelligence is reshaping medical imaging, pathology, radiation oncology, and chronic‑pain diagnostics in India. Each expert illustrated concrete use‑cases – from AI‑driven triage in pathology labs to deep‑learning models that cut assessment time for thoracic oncology patients. The discussion moved through three broad themes: (1) accelerating and personalising diagnosis, (2) early‑disease screening and predictive analytics, and (3) trust, explainability, and practical challenges of scaling AI across Indian hospitals and primary‑care settings. The session concluded with a candid exchange on data privacy, interoperability, and the roadmap for deploying low‑cost AI tools in rural health centres.

Detailed Summary

Moderator (Dr. M. K. Data) welcomed the audience, highlighted the interdisciplinary nature of the panel, and introduced the six panelists.
Dr. Sujit Gautam (Anaesthesiology, SGPGIMS) described his work on AI‑based chronic‑pain diagnostics for primary‑care settings, funded by an ICMR project.
Prof. Nidhi Goel (ECE, IGDTUW) outlined her background in deep learning, mentorship of six PhDs in medical AI, and a current collaboration with AI‑NS (AI & Nano‑Systems) on wireless capsule endoscopy for gastroenterology.
Prof. Chandan J. Das (Radiology, AIIMS) summarized four‑plus years of AI research on cancer imaging (gallbladder, kidney, prostate, colorectal) in partnership with IIT‑Delhi.
Dr. Kundan Singh Chufal (Radiation Oncology, Rajiv Gandhi Cancer Institute) highlighted a 40 % AI‑based publication record and a workflow that reduced patient assessment time from three days to one day for thoracic, sarcoma and lymphoma cases.
Dr. Alok Sharma (Pathology & EM, Lal PathLabs) explained the scale of Lal PathLabs, its responsibility to stay ahead with AI‑enabled routine diagnostics, and ongoing academic‑industry collaborations.

2. How AI Is Already Helping Clinicians

2.1 General Benefits (as framed by the moderator)

AI acts as a team‑member that analyses large, multimodal data sets, identifies patterns, and suggests diagnoses faster and more accurately.
It supports personalised treatment by matching patients to therapies that maximise therapeutic benefit while minimising side‑effects.

2.2 Domain‑Specific Illustrations

Domain	Speaker	Key Use‑Case(s)
Radiology	Prof. Chandan	AI models for detection, grading, and prognosis of gallbladder, kidney, prostate, colorectal cancers; opportunistic detection of incidental lung nodules in routine chest X‑rays.
Oncology (Radiation)	Dr. Chufal	AI‑driven decision support that reduced pre‑treatment assessment from 3 days → 1 day, enabling same‑day eligibility verification for radiotherapy.
Pathology	Dr. Sharma	Triaging: AI screens ~50 daily gallbladder resections, flagging ~8 high‑risk cases for detailed review. Inter‑observer variability: AI provides a “second expert” view, standardising histopathology reads. Mitosis counting & cell quantification: automation frees pathologists for higher‑order tasks.
Chronic Pain	Dr. Gautam	AI model integrated into primary‑care workflow to flag likely chronic‑pain conditions, reducing unnecessary investigations.
Capsule Endoscopy	Prof. Goel	Deep‑learning pipeline that interprets wireless capsule video streams, supporting gastro‑enterologists in early detection of mucosal lesions.
General Workflow	Dr. Data (moderator)	AI‑enabled clinical decision‑support and document summarisation that cuts report‑generation time from ~45 min to 2–3 min.

2.3 Quantitative Impact

Radiology: AI flagged 20 % of 300–400 daily X‑rays as abnormal, allowing radiologists to focus on the critical 20 % subset.
Oncology workflow: 40 % of Dr. Chufal’s publications in the last five years were AI‑centric; the workflow cut assessment time by 66 %.
Pathology triage: 8 of 50 gallbladder cases (≈16 %) identified for detailed review, improving pathologist efficiency.
Video analysis (AIMS project): Reduced review of 100 000 frames to ≈20 frames, shrinking expert review time from 4 h → ~3 min.

3. AI for Early Diagnosis & Population‑Level Screening

3.1 Predictive vs. Diagnostic Paradigm (Prof. Dutta – referenced)

Medicine is shifting from “what disease do you have?” to “what disease might you develop?”.
Whole‑slide imaging and large annotated datasets enable AI to recognise pre‑clinical morphologic signatures (e.g., early diabetic nephropathy, sub‑clinical cancer markers).

3.2 Opportunistic Detection (Prof. Chandan)

Embedding AI into routine imaging (e.g., chest X‑ray for respiratory complaints) resulted in 7–8 % detection of incidental lung nodules, with 25 % malignant.
This “opportunistic screening” leverages existing imaging pipelines without separate population‑wide screening programs.

3.3 Tier‑2 / Tier‑3 Implementation (Prof. Chandan)

Developed AI‑augmented ultrasound screening for gallbladder cancer in the “high‑risk belt” of Eastern India; using non‑expert technicians who acquire images that are auto‑analysed centrally.
Similar pipelines exist for prostate cancer grading (MRI), breast cancer detection (mammography), lung cancer (CT/X‑ray), and renal tumour grading.
Reported accuracies: ≈95 % for AI‑graded prostate MRI vs. ≈87 % for human radiologists.

3.4 Mobile‑App Screening (Audience contribution)

Roshni (Uttar Pradesh) – a mobile‑app that captures pupillary images to detect cataract, enabling early referral in remote settings.

4. Trust, Explainability, and Validation

4.1 Sources of Skepticism (Dr. Gautam & Prof. Goel)

Clinicians often trust AI ≈70 % (not 100 %) due to the “black‑box” nature.
Explainable AI (XAI) techniques (feature importance plots, attention heat‑maps) are gradually converting black‑boxes to “gray‑boxes”, improving clinician confidence.

4.2 Local vs. Imported Models (Prof. Goel)

AI models trained on Western datasets may under‑perform on Indian populations because of demographic, dietary, and disease‑prevalence differences (example: regional variations in gum disease).
Necessity for diversified, representative training data spanning North‑South‑East‑West, all age groups, and genders.

4.3 Continuous Monitoring & Data‑drift (Prof. Goel)

AI systems must be periodically re‑validated to account for shifts in data distribution (e.g., younger onset of cardiovascular disease).

5. Practical Challenges in Deploying AI in Indian Hospitals

Challenge	Speaker(s)	Illustrative Comments
Data privacy & governance	Dr. Chufal	Need robust consent frameworks and secure storage; regulatory clarity still evolving.
Interoperability	Dr. Chufal	Existing HIS/EHR systems are siloed; AI tools must integrate into a single clinician view rather than multiple, parallel dashboards.
Clinical workflow acceptance	Dr. Chufal, Dr. Gautam	Clinicians need training; “human‑in‑the‑loop” designs avoid the perception of AI replacing doctors.
Resource constraints in rural PHCs	Moderator & audience	High upfront cost; government‑funded pilots needed; solutions must run on low‑spec hardware and be operable by ASHA/ANM workers.
Standardised data capture	Prof. Goel	Uniform case‑record templates (age, gender, symptoms, labs) across private clinics and tertiary hospitals are essential for pooled‑learning.
Scalability & governance	Dr. Dutta (referenced)	Multi‑institutional consortia required to share de‑identified data and co‑develop national‑level models.

6. Roadmap for AI in Primary & Rural Healthcare

Government‑backed funding for low‑cost AI platforms (e.g., AI‑enabled triage apps for ASHA workers).
Standardisation of data capture across the public and private sectors – a national “AI‑ready” EMR schema.
Pilot programmes leveraging existing mobile health apps (Roshni, Vision‑AI for cataract) to demonstrate impact on maternal and child health metrics.
Capacity‑building – short courses for clinicians on AI basics, explainability, and ethical use.
Regulatory sandboxes to test AI models in controlled environments before wide‑scale rollout.

7. Audience Interaction & Emerging Topics

Genomics & AI – Dr. Himlata Bhatia (clinical geneticist, SRL Diagnostics) raised concerns about data ownership, privacy, and monetisation of DNA data.
- Discussed federated learning and retrieval‑augmented generation (RAG) as possibilities to train models without exposing raw genomic data.
- Emphasised that model weights themselves do not reveal patient‑level data, but governance must still enforce strict access controls.
Future of AI‑generated reports – Panelists concurred that fully autonomous reporting is unlikely in the near term; a human‑in‑the‑loop model remains the safest path.
Philosophical note – A brief exchange about the term “artificial intelligence” highlighted that AI is fundamentally a mathematical model (forward/back‑propagation), not “intelligence” per se, underscoring the need for realistic expectations.

8. Closing Remarks

Moderator thanked the panelists for sharing real‑world experiences, underscored the need for collaborative ecosystems, and invited the audience to continue the dialogue during the conference’s networking breaks.

Key Takeaways

AI accelerates diagnosis across specialties by triaging cases, highlighting abnormalities, and automating routine quantifications (e.g., mitosis counting).
Clinical decision‑support should remain human‑in‑the‑loop; current trust levels hover around 70 % pending further explainability.
Early‑disease screening is best achieved through opportunistic AI embedded in existing imaging workflows rather than dedicated mass‑screening programmes.
Local data is essential: Indian AI models trained on Western datasets suffer performance gaps; diverse, representative datasets are critical.
Explainable AI (XAI) tools (feature importance, attention maps) are turning black‑boxes into “gray‑boxes”, increasing clinician confidence.
Interoperability and data standards are the biggest bottlenecks; a single unified clinician dashboard is needed to avoid workflow fragmentation.
Rural deployment requires ultra‑low‑cost, easy‑to‑use tools (mobile apps, simple imaging kits) and strong government backing for initial capital outlay.
Privacy & governance must accompany any AI rollout; federated learning and RAG architectures can protect sensitive genomic or clinical data.
Collaborative consortia spanning North‑South‑East‑West institutions are necessary to pool data, create robust models, and achieve nationwide scalability.
AI is a tool, not a replacement; its greatest value lies in augmenting clinicians, expanding access, and ultimately saving more lives.

See Also:

India AI Impact Summit 2026

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