medical AI – INDTRAC Real Time Tracking

AI is changing almost every industry that we know of. In healthcare, it is proving to be widely disrupting - starting from the way the healthcare data is used to revolutionizing patient care and medical consulting. You may have already heard of deep impact of AI algorithm in predictive analysis in healthcare. Agentic AI takes it two steps forward. It integrates everything to give a unified and powerful service interface that you haven't seen yet.

Imagine while you are checking your patient, your assistant is already pulling all the reports for her, analyzing the data for predictive patterns that you are looking for, displaying that analysis in a visual format that you are most used it and suggesting possible options for you to pick and recording your advise while you explain that to the patient. The Agentic AI can be this super-efficient highly knowledgeable, smart assistant that you always wanted.

It's an AI system that doesn’t just help doctors and staff make decisions; it makes the decisions itself. It analyzes data, interprets information, and takes action—all with minimal human intervention. Imagine an AI that can read your patient’s medical chart, diagnose their condition, and recommend a treatment plan, all in real-time.

It can simplify regulation compliance for the hospital administrator. It can assist the operation head in analyzing and recommending the area that require closer scrutiny. It can collaborate with other agents in completing a tasks. It can take up the role of front desk agents when needed.

Cut the time in decision making

Imagine this: a patient comes into the emergency room with chest pain. Traditionally, a doctor would order a series of tests, wait for results, and—after all that time—determine if it’s a heart attack. That’s a lot of time for a patient who could be in serious danger. With Agentic AI, the AI can analyze all the data in minutes, compare it with thousands of similar cases, and flag the critical risks immediately. In fact, studies show AI systems can improve diagnostic accuracy by up to 30% compared to human doctors alone. That means fewer mistakes, quicker decisions, and better outcomes.

Fundamental elements of Agentic AI

It uses multi-modal Large Language Model as the engine (e.g. chatgpt or anthropic or Google Gemini) trained with specific body of knowledge
Autonomy in decision making - given a role, it can take its own decision based on the data it processed.
Mimics human goal-oriented behaviour - changes strategy in real time to fulfill the goal
Continuous learning - It learns on the job! While processing the data, creating the decision maps it also refines its inference engine based on new learning
It works in real time

The Dollars and Cents: What’s the ROI for a Mid-Sized Hospital?

There is no denying that Agentic AI will require sizeable investment.

On average, the cost of implementing Agentic AI in a mid-sized hospital can range anywhere from $1 million to $5 million, as per an estimate. That includes software, integration with existing hospital systems, and training staff to use the new tools.

But here’s where things get interesting: the ROI is rapid. Hospitals that adopt Agentic AI typically see a 20-30% reduction in administrative costs. With less paperwork, fewer human errors, and faster patient turnover, hospitals can start saving money in a matter of months.

Let's break it down to the avenues where it can help

Automating routine administrative tasks (like scheduling, billing, and patient inquiries) reduces the need for manual labor, cutting down on staffing costs.
Fewer diagnostic errors translate to fewer malpractice claims and a reduction in unnecessary treatments.
Better resource allocation means optimized bed usage and staffing, reducing the costs associated with inefficiencies.

Would this be another isolated system like EHR software? Actually the power comes in integrating the agentic AI into your back-end so that it can 'see' the patterns in the data-stream and alert you on beforehand.

Commodity Agentic AI for Hospital

A commodity Agentic AI is expected to be universally usable for every hospital without needing to train the model. Microsoft recently launched Dragon Copilot, a voice-activated AI assistant which Microsoft claims, "has helped clinicians document billions of patient records, and has assisted over 3 million ambient patient conversations across 600 healthcare organizations in the past month alone. With these ambient AI capabilities, organizations have already realized significant outcomes, with clinicians reporting five minutes saved per encounter,[1] 70% of clinicians reporting reduced feelings of burnout and fatigue,[2] 62% of clinicians stating they are less likely to leave their organization,[3] while 93% of patients report a better overall experience."

Pitfalls?

There is one area of concern that everyone is conscious about. It can potentially give access (to the hospital's internal data) to Agentic AI provider. There are both technical and legal remedies available to mitigate that risk.

Summary

According to a report by Deloitte, AI in healthcare has the potential to generate $150 billion in annual savings for the US healthcare system alone.The thing is that the technology is almost here with the most competitive lot already moving ahead of the curve. Better care, smarter decisions, streamlined operations, and lower costs—that’s the power of Agentic AI.

For a hospital’s managing director, most crucial and expensive resource is doctors’ bandwidth. Imagine an ailing patient entering the hospital for the first time. In US, a GP would visit him and run the first-level diagnosis. Based on his analysis a line of treatment would be indicated or the patient would be referred to a specialist.

In India, the patient would go to the the front desk and they will guide to the appropriate department based on patient’s answers.

In both cases, It is humanly impossible to remember about vast number of medical issues with correlated symptoms. This means that initial diagnosis could take a little while, sometimes at the cost of patient’s time and patience.

Now imagine that front-desk is equipped with doctor-grade AI or the GP is assisted with doctor-grade AI, like ChatGPT, trained on vast corpus of medical data and researches. It is somewhat like all the specialists are standing behind the GP to help him reach correct diagnosis at the quickest time possible.
With the help from AI, pin-pointing the right department would be faster for the front-desk assistant. AI can help the GP handle a lot more load with higher accuracy.

Medical Generalist AI

With faster initial diagnosis, hospital’s productivity increases by magnitude. The fact is technology already has reached this level. Commercialization may take another year for the regulation bodies to create guard-rails for the use of such technology ubiquitously. But advanced hospitals may start utilizing the generalist AI service much earlier.

DeepMind Med-PaLM

Particularly two companies are at the forefront now. Google’s DeepMind is almost ready with Med-PaLM. Med-PaLM is a multi-modal large language model (LLM) trained on vast medical data. In the recent report it claims to be 90% accurate in its responses. It’s repertoire is vast if not comprehensive. It can understand Clinical Languages. It can analyze images (radiology etc). It is claimed to speak Genomics too!

OpenEvidence

And there is OpenEvidence, promoted by Mayo Clinic. Theirs’ also is a multi-modal large language model but with an extra chip on its shoulder. Unlike other LLMs like ChatGPT, it is trained with real-time data. With real-time data streaming into it, it would be up-to-date about latest medical findings on every sector –or that’s what they claim when the service will be commercially ready. It could be very soon since they recently claimed[2] it to score above 90% in United States Medical Licensing Examination.

OpenEvidence AI claims to become the First AI in History to Score Above 90% on the United States Medical Licensing Examination (USMLE)

Opportunity or Threat?

GPOnline finds that England needed nearly 7,400 more full-time GPs to manage the patient care. According to a 2021 WHO report, India was just above the 2006 standard of 22.8 healthcare workers per 10,000 population. With professional doctor-grade generalist AI at disposal of every hospital, relief is not going to be quantitative alone, it can add qualitative improvements in patient diagnosis in terms of reducing diagnostic time, improving accuracy and potentially reducing number of tests for the patient.

With a professional team always monitoring the model’s answers and fine-tuning the model’s learning, the improvement will be continuous and so will be the efficacy of the system.
Following the current trend, entire service most likely will be made available as SaaS for the hospitals and clinics. SaaS model also will help bringing incremental cost of adoption down to an affordable basis.

In summary, it looks like that generalist medical AI is going to bring paradigm-shift in patient care in not a very distant future. How pervasive the adoption would be though would depend on a hospital’s patient-care economics.

1.DeepMind announces Med-PaLM
2.First AI in History to Score Above 90%
3.OpenEvidence official site
4. Med-PaLM official page

Tag Archives: medical AI

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