Clinical AI Specialist Doctor in Hyderabad, India: Dr. Ram Kumar G
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A clinical AI specialist doctor connects medicine with machine learning: framing the clinical question, checking the data, testing whether a model is safe and useful, and fitting it into real care. Here is what that work involves, the areas it covers, and how hospitals, health-tech teams, researchers and educators across Hyderabad can get in touch.
How a clinical AI enquiry usually works
Send a short email
Write to ramakumar.md@gmail.com or message on LinkedIn with your organisation, the question or project, and any timeline.
An initial conversation
A short call to understand the clinical problem, the data involved and what a useful outcome would look like for your team.
Agree the scope
What will be reviewed, designed or taught, who is involved, and how patient data will be protected, agreed in writing before work starts.
The work itself
For example a review of an AI tool's evidence, input on study design, a clinical safety review of a product, or a workshop for clinicians.
Written summary and follow-up
Findings and recommendations in plain language, with sources, and a follow-up discussion if questions remain.
What a clinical AI specialist doctor does
Most AI projects in healthcare are built by engineers and data scientists. A clinical AI specialist doctor brings the clinical side: knowing what a diagnosis code really means in a hospital record, which errors would harm a patient, and whether a model's output would change a decision at the bedside.
In practice the work sits between clinicians, data teams, managers and regulators. It covers framing the right clinical question, checking that the data reflect real care, choosing fair ways to test a model, spotting bias and safety risks, and planning how a tool fits into the daily workflow of doctors and nurses. Just as important is the honest answer that, for some problems, AI is not the right tool at all.
Background in medicine, epidemiology and machine learning
Dr. Ram Kumar G combines clinical practice with public-health training and hands-on machine learning work. The details below are from his professional profile; the About page lists his full qualifications and medical registration.
| Area | Experience |
|---|---|
| Clinical medicine | MBBS; MD (Internal Medicine), Mediciti Institute of Medical Sciences; 10+ years as a physician and diabetologist |
| Current role | Senior Consultant, Diabetes and Metabolic Health, Boston Diabetes Center, Gachibowli, Hyderabad |
| Epidemiology and public health | Master's in Public Health (MPH), University of Nevada, Las Vegas, USA |
| Health data and analytics | Program Analyst, New Mexico State Department of Health, USA (2 years) |
| Clinical research and machine learning | 4 years of experience |
| Digital health | Consultant physician, Department of Telemedicine, Apollo Hospital, Hyderabad (4 years) |
| AI training | Stanford certificates: AI in Healthcare; Evaluation of AI Applications in Healthcare; Fundamentals of Machine Learning in Healthcare |
| Teaching | Author of the free Medical AI course on this website |
Clinical AI areas covered
Clinical AI is not one technology. The areas below are the ones most often involved in hospital, research and product work, and each has a matching track in the free Medical AI course on this site.
| Area | Examples | Course track |
|---|---|---|
| Classical machine learning | Readmission and risk prediction, logistic regression, tree-based models | Machine learning |
| Deep learning and imaging | Retinal photographs, chest X-rays, foot-ulcer images | Deep learning & imaging |
| Clinical NLP | Extracting facts from notes and discharge summaries; de-identification | Clinical NLP |
| Generative AI and LLMs | Drafting summaries and letters, prompt design, hallucination risks | Generative AI & LLMs |
| Retrieval-augmented generation | Answers drawn from guidelines and hospital documents, with citations | RAG |
| Agentic AI | Multi-step tools with safeguards and human sign-off | Agentic AI |
| Evaluation and deployment | Validation, calibration, fairness, monitoring after go-live | Evaluation, safety & deployment |
| Regulation and ethics | CDSCO, ICMR guidelines, DPDP Act, EU AI Act, US FDA | Regulation, ethics & governance |
| Health data and standards | FHIR, HL7, ICD, SNOMED CT, LOINC, privacy | Foundations |
| Applied diabetes AI | CGM analytics, retinopathy screening, risk scores | Applied case studies |
Prediction models
Risk scores and machine learning models built from health records
Medical imaging
Deep learning for retinal photographs, X-rays and wound images
Language models
Clinical NLP, generative AI, retrieval-augmented generation and agents
Safety and regulation
Validation, bias, monitoring, CDSCO, ICMR guidance and data protection
Diabetes and metabolic AI
CGM data, retinopathy screening and diabetes risk models
Clinical workflow
Fitting a tool into real care, with clinicians in control
Who can get in touch, and for what
Enquiries are welcome from organisations and individuals across Hyderabad. These are the kinds of requests that are a good fit:
| Who | Typical requests |
|---|---|
| Hospitals, clinics and diagnostic centres | Reviewing the evidence behind an AI tool before buying it; planning a pilot; governance and data-protection questions |
| Health-tech startups | Clinical input on the problem a product solves; clinical safety review; study design for validation; regulatory awareness |
| Researchers and universities | Collaboration on clinical machine learning and epidemiology studies; study design; analysis and papers |
| Medical colleges, conferences and developer groups | Workshops, lectures and talks on clinical AI for doctors, students and developers |
Is an AI tool ready for your hospital?
Many AI tools perform well in the setting where they were built and less well elsewhere. Before a tool is used in care, a few questions decide whether it is ready, needs local testing, or is not needed at all. The WHO's guidance on AI for health and ICMR's 2023 ethical guidelines both stress validation and human oversight before clinical use.
An AI tool is proposed: is it ready for use?
An AI tool is proposed for your hospital, clinic or service
Does it solve a clearly defined clinical or operational problem?
If No
Define the problem first; an AI tool may not be needed
If Yes
Has it been validated on patients and data like yours?
If No or unclear
Ask for local validation or an independent clinical review before use
If Yes
Check regulatory status, data protection and who is accountable
Run a supervised pilot with monitoring, then decide on wider use
Healthcare AI across Hyderabad and Telangana
Hyderabad has a large concentration of hospitals, life-sciences companies and technology firms, which is why clinical AI questions come up here so often. Genome Valley is one of the country's major life-sciences clusters, the city hosts the annual BioAsia summit, whose 2026 edition focused on AI, automation and data science in biology, and incubators such as T-Hub and the Centre for Innovation and Entrepreneurship at IIIT Hyderabad support health-tech startups working with AI.
Enquiries are welcome from anywhere in the city and its surroundings, including HITEC City, Madhapur, Kondapur, Gachibowli, the Financial District, Banjara Hills, Jubilee Hills, Begumpet, Ameerpet, Secunderabad, Kukatpally, Uppal and Genome Valley.
Principles for responsible clinical AI
Every engagement follows the same basic principles:
- AI supports clinicians; it does not replace clinical judgement or a doctor's responsibility for care
- A tool is tested on data like the patients it will serve before it is trusted, and monitored after it goes live
- Vendor neutrality: models, libraries and companies are discussed on their evidence, never ranked or endorsed
- Identifiable patient data is never pasted into public AI tools, and projects follow India's Digital Personal Data Protection Act, 2023, whose main obligations take effect from May 2027 (as of October 2026)
- Regulatory facts are stated with a date and re-checked, because rules for AI software are changing quickly in India and abroad
AI output is not medical advice
- AI tools, including chatbots and the code in the Medical AI course, are not validated medical devices unless a regulator has approved them for that use. Clinical decisions should stay with qualified clinicians.
Free clinical AI learning resources
For clinicians, students and developers who want to learn first, the free Medical AI course on this site covers the areas above in ten tracks, from foundations and data standards to evaluation, regulation and applied diabetes case studies. Each lesson cites its sources, and exercises use synthetic or openly licensed data only.
A good starting point is the Start here page, followed by the practice perspective on questions to ask before adopting AI in a diabetes clinic.
How to contact Dr. Ram Kumar G about clinical AI
Email ramakumar.md@gmail.com or send a message on LinkedIn. A useful first email includes your organisation, the question or project, the type of data involved (without any patient details), and your timeline.
This email is for clinical AI work. For medical appointments, patients should contact the practice: Boston Diabetes Center, Gachibowli, Hyderabad, on 09618994555 (call or WhatsApp), Monday–Saturday, 12 PM–8 PM.
Please don't send patient data by email
- Don't include names, record numbers, reports, images or any other identifiable patient information in an enquiry. If a project needs patient data, how it will be protected is agreed before any data is shared.
When to seek care
- Emergency: chest pain, severe breathlessness, sudden weakness on one side, a drooping face or slurred speech. Call emergency services rather than asking an AI chatbot
- Same day: very high or very low blood sugar readings, or confusion in someone with diabetes
- Prompt appointment: symptoms that continue or worsen after an AI tool described them as minor
- Prompt appointment: before starting, stopping or changing any medicine an AI tool suggested
FAQ
What is a clinical AI specialist doctor?
A doctor who works on the use of artificial intelligence in healthcare, bringing clinical knowledge to how AI tools are designed, tested and used. The role links clinicians, data scientists, managers and regulators.
Who is a clinical AI specialist doctor in Hyderabad?
Dr. Ram Kumar G is a Hyderabad physician (MBBS, MD Internal Medicine) with a Master's in Public Health from the USA, Stanford certificates in AI in healthcare, and 4 years in clinical research and machine learning. He can be contacted at ramakumar.md@gmail.com.
Can a hospital get help evaluating an AI tool before buying it?
Yes. A review typically looks at the clinical problem, the evidence and validation behind the tool, how it would fit the workflow, regulatory status and data protection, and how it would be monitored.
Do you work with health-tech startups in Hyderabad?
Yes. Startups can ask for clinical input on the problem their product solves, a clinical safety review, help designing a validation study, and an overview of the regulatory landscape.
Is research collaboration possible?
Yes. Researchers and universities can get in touch about clinical machine learning and epidemiology studies, from study design to analysis and writing.
Are workshops or talks on clinical AI available for doctors and students?
Yes. Medical colleges, conferences and developer groups can request workshops, lectures or talks. The free Medical AI course on this site can also be used alongside them.
How is clinical AI regulated in India?
As of October 2026, software with a medical purpose, including AI software, is regulated as a medical device under the Medical Devices Rules, 2017, and CDSCO has issued specific guidance on medical device software. ICMR's 2023 guidelines cover ethics, and the DPDP Act, 2023 covers personal data.
Which areas of Hyderabad do you cover?
Enquiries are welcome from anywhere in Hyderabad and Secunderabad, from HITEC City, Madhapur and Gachibowli to Banjara Hills, Kukatpally, Uppal and Genome Valley. Email ramakumar.md@gmail.com or message on LinkedIn.
Sources
- WHO: Ethics and governance of artificial intelligence for health: guidance on large multi-modal models (2024)
- WHO: Ethics and governance of artificial intelligence for health (2021)
- ICMR: Ethical guidelines for application of AI in biomedical research and healthcare (2023)
- Emergo by UL: India's CDSCO finalizes guidance on medical device software
- BioSpectrum: BioAsia 2026 focuses on Global TechBio
Medical disclaimer: Content on this page is for general education and is not a substitute for individualized medical advice, diagnosis, or treatment. Outcomes vary between patients. Boston Diabetes Center is outpatient / day-care specialty centre with an on-site diagnostic lab and pharmacy — no inpatient beds; imaging and inpatient care are coordinated through partner facilities.