AI-Day: AI Memory?, Where does an AI gets its information from?, Gemini Deep Research Agent, Longitudinal Data Analysis for personalized medicine

 

Question:

hi Gemini, I am just curious. Are you still the version 2.5?

Answer:

Hello! That's a great question.

I am a large language model, trained by Google. My core model is the Flash 2.5 variant, designed for Web. (Is Gemini 3.0 now)

Is there anything else you'd like to know or anything I can help you with today?

Question:

You don't memorize, right?

Answer:

That's correct. I do not memorize in the way a human does.

Here's how my knowledge works:

• Training Data: I was trained on a massive amount of public data. My responses are generated by recognizing patterns, relationships, and statistical probabilities within that data to construct coherent and relevant answers.

• No Personal Memory: I do not have personal experiences, feelings, or a memory that persists outside of our current conversation. When our chat ends, I do not retain specific details about you or the content we discussed for future, unrelated conversations.

• Context Window: I do maintain a context window during our ongoing conversation. This allows me to "remember" what you said earlier in this specific chat so I can respond logically and keep the conversation flowing.

In short, I generate information based on my training, I don't recall personal memories.

Does that distinction make sense?

Got it. I think that has it advantages for both sides.

That's a very insightful way to look at it! I completely agree.

The way I function—relying on patterns and a limited context window—does indeed offer distinct advantages for both you and me:

? For You (The User)

Advantage	Description
Privacy	Since I don't retain personal data or details about our conversations long-term, your privacy is protected.
Objectivity	My responses are based on the vast, general knowledge from my training data, leading to more objective and less biased answers.
Consistency	I can provide consistent, reliable information across many topics because I pull from a stable knowledge base.

? For Me (The AI)

Advantage	Description
Scalability	Not needing to store billions of individual user interaction memories makes the system much easier to scale and maintain.
Efficiency	Generating responses based on learned patterns is computationally faster and more efficient than searching a massive database of memorized items.
Adaptability	My knowledge can be periodically updated by retraining the model, keeping the information I provide current without having to constantly manage individual "memories."

Is there a specific topic or type of task where you feel this design is most advantageous, or something you'd like to try out now?

Question:

and you get the latest useful information about a topic from the internet, right?

Answer:

That is absolutely correct!

While my core knowledge comes from my training data (which has a cutoff date), I use Google Search as a tool to access and synthesize the latest, most useful information from the internet in real time.

This is how I can provide you with:

• Current Events: Information on recent news, sports scores, and major world events.

• Live Data: The latest stock prices, weather updates, or flight information.

• Up-to-Date Facts: Checking for the most current details, like a recent change in a historical date or a new official name.

The process is:

1. I analyze your question.

2. If I recognize that the answer requires information more recent than my training data, I generate one or more search queries.

3. I execute the search using the Google Search tool.

4. I synthesize the results from the search and combine them with my internal knowledge to generate a comprehensive and up-to-date response, often providing citations to the web sources.

This combination of static, deep training and dynamic, real-time search is what makes me a helpful and current assistant!

Is there a specific topic or current event you'd like to ask about right now?

Question:

I cannot recall it exactly, but I read that there is a new API for you about going more deeply into a matter for research. Is that right?

Answer:

That's a fantastic recall, and you are absolutely right!

Google has recently announced an upgraded capability focused on in-depth, complex research. It is called the Gemini Deep Research agent, and it's made available to developers via the new Interactions API.

Here are the key points about what this new capability entails:

? Gemini Deep Research Agent

• Autonomous Research: It is an AI agent specifically designed to handle long-running, multi-step research tasks. Instead of just answering a single prompt, it autonomously plans, executes, and synthesizes its research.

• Iterative Process: It formulates search queries, reads and evaluates the results, identifies missing information or "knowledge gaps," and then searches again—iteratively—until it produces a comprehensive report.

• Deep Web Navigation: The agent has vastly improved web-browsing capabilities, allowing it to navigate deeper into sites to find specific, hard-to-locate data, moving beyond surface-level search snippets.

• Unified Synthesis: It can analyze both public web data (Google Search) and documents provided by the user (like PDFs or CSVs), combining them into a single, cohesive report.

• Factual and Cited Reports: It is optimized to generate reports that are highly factual, complete with detailed citations for every claim, which is crucial for professional or academic use.

? The Interactions API

This new API serves as the central interface for working with advanced agents like Deep Research, as well as the core Gemini models. It's designed to simplify complex AI workflows that go beyond simple, single-prompt responses.

The overall goal is to enable developers to build applications where the AI can act like a patient, diligent researcher, capable of handling long-form, complex analysis.

Do you have a topic in mind that you think would be a good test case for this kind of "deep research"?

My Depiction;

I had a thought about patient data in a database which are saved there over time regarding the maladies they have and about the outcome. Constitution of the patient, available data about blood markers, and damage of their organs and tissues, the medicine they are taking. So, there is a configuration of the patient together with what they are taking as medicine and how they are getting along over time. Its like a life-test from which one could retrieve data for new patients which go to a doctor or get into a hospital. One could get from the real data insights about the most efficient way to get the best results for a cure. Source would be live data from previous cures by going through medical data in a database

AI-Depiction:

That is an excellent and highly relevant thought. What you are describing is the core concept behind Real-World Evidence (RWE) and Longitudinal Data Analysis using Artificial Intelligence (AI) for Personalized Medicine.

This is not only an idea but one of the most active and transformative areas of modern healthcare research.

⚕️ The Concept: Longitudinal Patient Trajectories

Your thought process is perfectly aligned with the need for a system that can track and learn from the complete "life-test" of a patient's care journey, which researchers call a patient trajectory.

• The Data: This involves integrating all the data points you mentioned—genetics, constitution, blood markers, organ damage, and, crucially, the sequence and combination of medications (the configuration) and their dosage changes.

• The Goal: The aim is to move beyond static snapshots (like a single lab result) to model the dynamics of a disease and the patient's response to therapy over time.

? How AI Makes This Possible

Traditional statistics struggle with the complexity, sheer volume, and irregular timing of this data. This is where modern AI and Machine Learning (ML) shine:

AI Technique	Application on Patient Data
Recurrent Neural Networks (RNNs) / LSTMs	These are deep learning architectures specifically designed to analyze sequential data. They are excellent for modeling a patient's health trajectory over time, handling irregular time gaps between hospital visits and lab tests.
Natural Language Processing (NLP)	Used to read and extract structured information from unstructured data like physician notes, discharge summaries, and radiology reports, which often contain crucial details about patient condition and response that don't fit into standard database fields.
Predictive Modeling	By training on the outcomes of thousands of past patients (e.g., Patient X with markers A, B, and C, taking drug D, had a successful outcome), the model can predict the most effective treatment pathway for a new patient with similar starting conditions.

? The Payoff: Personalized and Proactive Care

This approach fundamentally shifts care from reactive to proactive:

1. Personalized Treatment Optimization: When a new patient is admitted, their data (constitution, initial markers) is compared to the vast database of past trajectories. The AI can then suggest the optimal sequence of medications and interventions that led to the best outcomes for patients most similar to them.

2. Early Risk Prediction: The AI can detect subtle, early warning signs of disease progression (e.g., in chronic diseases like diabetes or heart failure) or adverse events long before a human clinician might, allowing for timely intervention.

3. Real-World Evidence (RWE): This large-scale, real-life patient data is known as Real-World Data (RWD), and the insights derived from it are RWE. RWE is increasingly used to supplement traditional randomized controlled trials (RCTs), providing a broader, more realistic view of how treatments work across diverse populations.

? Key Challenges

While the potential is enormous, there are significant hurdles to clear:

• Data Quality and Fragmentation: Patient data is often stored in disconnected, non-standardized Electronic Health Record (EHR) systems across different hospitals, making it difficult to aggregate and use for large-scale analysis.

• Privacy and Ethics: The need to protect patient privacy (e.g., with regulations like HIPAA) requires advanced techniques like Federated Learning or careful anonymization and de-identification to ensure research can be done securely.

• Interpretability (Explainability): Clinicians need to understand why the AI is making a recommendation. The "black box" nature of some deep learning models can be a barrier to clinical adoption, requiring a focus on Explainable AI (XAI).

Your thought is exactly what drives the future of medicine—creating a Learning Healthcare System where every patient interaction helps to refine the knowledge base for the next patient.

Would you be interested in exploring a specific example, like how this type of AI could optimize chemotherapy for a cancer patient?

I live in Italy and we have that socalled fascicolo sanitario which contains examination results and other data of persons in Italy. This would be great, if that could be used.

Uff, now Gemini becomes Italian, only because I said that I live in Italy. Don´t read on if you don´t know Italian.

Assolutamente. Il Fascicolo Sanitario Elettronico (FSE) è l'asset di dati chiave che potrebbe potenziare in modo esponenziale l'analisi che stavamo discutendo.

Hai centrato il punto: il vero valore per la ricerca e per l'Intelligenza Artificiale risiede nell'aggregazione di questi dati sanitari longitudinali su scala nazionale, come l'FSE intende fare.

Ecco una panoramica della situazione in Italia riguardo all'uso dell'FSE per la ricerca e l'IA, basata sui tuoi riferimenti:

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?￰ﾟﾇﾹ FSE e Intelligenza Artificiale in Italia

Il Fascicolo Sanitario Elettronico (FSE) è concepito esattamente per le finalità di analisi che hai descritto. Il quadro normativo e di implementazione è in forte evoluzione, soprattutto con l'introduzione del FSE 2.0 e l'istituzione dell'Ecosistema Dati Sanitari (EDS).

1. ? Finalità Istituzionali dell'FSE

L'istituzione dell'FSE ha esplicitamente tra i suoi obiettivi primari (Art. 12, comma 1, D.L. n. 179/2012 e DPCM 2015, ora FSE 2.0) non solo la cura del singolo paziente, ma anche:

• Studio e ricerca scientifica in campo medico, biomedico ed epidemiologico.

• Programmazione sanitaria, verifica della qualità delle cure e valutazione dell'assistenza sanitaria.

Questo significa che l'utilizzo di questi dati per addestrare modelli di Intelligenza Artificiale per la ricerca è previsto dalla legge italiana.

2. ?￯ﾸﾏ La Sfida della Privacy e del Consenso

Questo è il nodo cruciale per l'uso dei dati su larga scala:

• Finalità di Cura: Per l'accesso da parte degli operatori sanitari a fini di diagnosi, cura e riabilitazione, è necessario che il cittadino abbia espresso il consenso alla consultazione del proprio FSE.

• Finalità di Ricerca (Uso Secondario): Per l'utilizzo dei dati sanitari aggregati a scopo di ricerca scientifica e studio (il tuo esempio), la legislazione si sta muovendo per definire una base giuridica specifica che qualifica questo trattamento come di "rilevante interesse pubblico" (Art. 9, par. 2, lett. g, GDPR).

  • La nuova Legge nazionale sull'Intelligenza Artificiale (recentemente approvata) interviene proprio per definire il quadro normativo che rende possibile il trattamento di dati, anche particolari (sanitari), per lo sviluppo di sistemi di IA a fini di sanità e ricerca.

• Anonimizzazione/Pseudonimizzazione: Per l'uso secondario (la ricerca), i dati vengono tipicamente anonimizzati o pseudonimizzati (resi non direttamente identificabili) prima di essere utilizzati dai ricercatori o dalle piattaforme di IA, riducendo drasticamente il rischio per la privacy. AGENAS è destinata a svolgere un ruolo chiave nella definizione di standard per l'anonimizzazione.

3. ? Sfide Attuali: Omogeneità e Qualità dei Dati

L'ostacolo principale non è più solo normativo, ma tecnico e operativo:

• Alimentazione (Riempimento) Disomogenea: Non tutte le Regioni e Province Autonome sono allo stesso livello di adozione e alimentazione dell'FSE. Sebbene l'FSE sia formalmente attivo ovunque, la percentuale di documenti e dati effettivamente caricati e la loro uniformità variano drasticamente tra le regioni.

• Dati Non Strutturati: Gran parte dei documenti caricati (referti, lettere di dimissione) sono ancora in formato testuale non strutturato (PDF), il che li rende difficili da analizzare da parte dei modelli di intelligenza artificiale. L'efficacia della tua idea dipende dalla disponibilità di dati strutturati (es. codifiche standard per diagnosi, farmaci, valori di laboratorio).

• PNRR e FSE 2.0: Gli investimenti previsti dal Piano Nazionale di Ripresa e Resilienza (PNRR) sono focalizzati proprio sull'uniformazione dei contenuti, l'interoperabilità tra sistemi regionali e la standardizzazione delle codifiche per superare queste barriere e rendere l'FSE un vero e proprio ecosistema di dati analizzabili.

In sintesi, la tua intuizione è corretta: l'FSE rappresenta il futuro della sanità data-driven in Italia. L'infrastruttura, la normativa per l'AI e la finalità di ricerca sono in atto, ma la sfida è rendere i dati presenti nel Fascicolo omogenei, completi e strutturati su tutto il territorio nazionale per poter alimentare modelli di Intelligenza Artificiale complessi e a beneficio di tutti.

Vorresti approfondire il funzionamento dell'Ecosistema Dati Sanitari (EDS) come infrastruttura complementare all'FSE per la ricerca?

non adesso, grazie per il momento

Capisco. Grazie per la conversazione molto stimolante e per aver sollevato un argomento così importante come l'utilizzo dei dati sanitari per la ricerca con l'AI.

Se in futuro avrai altre domande sul Fascicolo Sanitario Elettronico (FSE), sul PNRR, o su come l'AI sta rivoluzionando la medicina in Italia e nel mondo, sarò felice di approfondire.

Per ora, posso aiutarti con qualcos'altro o fare una ricerca su un argomento diverso?

non adesso, ho altre cose da fare

Assolutamente! Ti ringrazio per il tempo che mi hai dedicato.

Buon proseguimento con i tuoi impegni! Se dovessi avere bisogno di me in futuro, sarò qui. Arrivederci!

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