Jivesh Sharma, M.D.

@Jay1075

Medical oncologist. Founder & CEO @nexgenprecision. Building healthcare technology for patients and clinicians. AI evidence and vendor research at

Dallas, TX

Joined February 2009

115 Following

37 Followers

190 Posts

Pinned Tweet

Jivesh Sharma, M.D.

@Jay1075

5 days ago

I’m introducing https://t.co/LsMtsofd76 from @nexgenprecision: AI vendor research and sourced briefs for better healthcare decisions. First: AI CPT codes. Coding ≠ coverage ≠ payment. Free brief: https://t.co/3hNmE6Sski Request updates: https://t.co/d4YJQoaLfo #HealthcareAI

101

Jivesh Sharma, M.D.

@Jay1075

2 days ago

Here is the full visual edition of HealthIT podcast Episode 2: Healthcare AI—Where We Stand, September 2026. Watch the narrated slides here. Sources and transcript: https://t.co/rNWerWWeEZ Narrated with an AI-generated version of my own voice. #HealthcareAI #HealthIT

Jivesh Sharma, M.D.

@Jay1075

2 days ago

Where does healthcare AI stand in September 2026? In HealthIT podcast Episode 2, I examine adoption, financing, major companies and the evidence behind practical value. Watch or listen on Spotify: https://t.co/9kLsJHeN16 #HealthcareAI #HealthIT

Jivesh Sharma, M.D.

@Jay1075

24 days ago

@elonmusk “The Elon” 🤴

Who to follow

Charles Henderson

@ChuckatADA

Fighting for a world without #diabetes│When WE FIGHT together, we change lives!

Mike Rochelle

@RochelleMike

Your Good Life Development Partner | Human & Digital Transformation | IT & Microsoft Leader | Driving growth w/ a human and process touch to maximize ROI.

Linda

@LTheDogLover

Ex dog groomer, now just a lot of free time :)

Jivesh Sharma, M.D.

@Jay1075

over 1 year ago

Agentic AI in Oncology: The Next Leap in Cancer Care? 2025 is the year for the advent of AI agents. AI in oncology has already improved diagnostics and treatment selection. But what happens when AI becomes agentic—able to reason, learn, and act autonomously in clinical decision-making? Today’s AI models analyze vast datasets, but they still rely on human oversight. The next evolution is AI that actively monitors disease progression, adjusts treatments in real time, and even advocates for patients in clinical trials and insurance approvals. Where Agentic AI Could Transform Oncology: -Autonomous Cancer Screening – AI could track imaging over time, flag changes, and schedule follow-ups automatically. -Adaptive Treatment Planning – Instead of static recommendations, AI could adjust regimens based on biomarkers and side effects over time. - Intelligent Clinical Trial Matching – AI could predict eligibility changes and engage trial coordinators directly. -AI Patient Advocacy – AI could handle insurance claims, appeals, and financial assistance navigation. The potential is massive, but so are the challenges: trust, accountability, and regulatory oversight. How do we ensure safety, bias mitigation, and ethical implementation as AI shifts from an assistant to an active decision-maker? Agentic AI isn’t the future—it’s already here. The question isn’t if it will transform oncology, but how fast and how responsibly we adopt it. #AI #Oncology #AgenticAI #HealthTech #ClinicalDecisionMaking

Jivesh Sharma, M.D.

@Jay1075

over 1 year ago

AI in Clinical Trials: The Matchmaker That Could Save Lives Clinical trials are the backbone of oncology innovation, yet 80% fail to meet enrollment targets. Patients who could benefit often remain unaware of trials they qualify for, and oncologists struggle with complex eligibility criteria. AI is changing that. Emerging models can scan millions of patient records and match individuals to trials in real time, dramatically reducing recruitment bottlenecks. For instance, the National Institutes of Health developed TrialGPT, an AI algorithm that successfully identifies relevant clinical trials for potential volunteers, providing clear summaries of eligibility. But will oncologists and patients trust AI-driven recruitment? Concerns around data privacy, bias in algorithm training, and physician adoption remain. If AI could reduce trial recruitment timelines by months it would have a major impact on drug development. We could see faster approvals and access to life-saving therapies! #AI #Oncology #ClinicalTrials #HealthTech

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Jivesh Sharma, M.D.

@Jay1075

over 1 year ago

AI in Oncology: A New Era of Clinical Decision-Making Clinical decision-making in oncology is at an inflection point. Historically, decisions have relied on physician expertise, clinical guidelines, and a growing body of research. But as cancer care becomes more complex—with multi-omic profiling, targeted therapies, and dynamic treatment landscapes—AI is proving to be more than just an assistant; it’s becoming a force multiplier for oncologists. Recent studies, including our own team’s research presented at NCCN, demonstrate that AI-driven models can match or even surpass physicians in specific domains of decision-making. In our study, an AI tool achieved 100% accuracy in recommending genetic testing for hereditary cancer risk—outperforming prior clinician studies. This isn’t just about efficiency; it’s about ensuring that every patient receives the most precise, evidence-based care possible. But AI’s role in oncology is not just about accuracy—it’s about scalability. Oncologists face rising caseloads, evolving guidelines, and an explosion of data. The right AI tools can integrate this information in real time, reducing cognitive overload and improving decision consistency. However, trust remains a key barrier. To integrate AI effectively, we need clinician-in-the-loop systems, continuous validation against real-world data, and clear regulatory pathways. The question is no longer if AI will transform oncology decision-making, but how we ensure it does so responsibly and equitably. AI isn’t replacing oncologists—it’s augmenting us. And for the future of cancer care, that’s a paradigm shift worth embracing. #Oncology #AI #ClinicalDecisionMaking