Welcome to Processing #001 beta, the first draft of a publication by the Automate.clinic team. Because it’s tempting in tech to treat every advance like it just happened, like magic, we wanted to start with chronicling AI in healthcare from day zero.

When we pause we are reminded breakthroughs rarely arrive clean. They’re stacked on old systems, outmoded research, and ideas that didn’t die. This only compounds when we examine the complex histories of clinical practice advancements with that of AI. Their pasts aren’t only preamble—they shape our now, define what works, what we trust, and what we need to build next.

This issue is a timeline, but it’s also a kind of anti-amnesia. A way to remember that AI has been evolving alongside medicine for a long time. Before we move to a faster cadence, we’re looking to publish every two weeks, we wanted to slow down and do the due diligence: catch up, connect the dots, and show where healthcare tech work divergence from other fields begins.

We decided to build as much as possible out in the open, and would love your help: What milestones, anecdotes, or learnings are missing from this rewind?

E-mail us at processing@automate.clinic and we'll credit you if your contribution makes it into the first email.

The AI healthcare revolution started 75 years ago

What did a wide scan to compile a selective, incomplete history, but it still had value for us: The milestones, breakthroughs, regulation shifts, dead ends, and weird stories reminded us that progress isn’t linear. When things got dry and complex, we decided to add asides, to ground progress in stories and examples that let us better understand what it meant for us.

1950s | From thinking machines to Artificial Intelligence

4 mathematicians Claude Shannon, John McCarthy, Nathaniel Rochester and Marvin Minsky organize a weeks long brainstorm about “thinking machines”. During planning they land on the term Artificial Intelligence as part of the title for the event that is joined by a handful of other researchers. This workshop is deemed instrumental in establishing AI as a distinct field of study, with one area for rich potential being staked on medical diagnostics. The mentioned event organizers are later considered as members of the founding fathers of AI.

# aside: Then Assistant Professor of Mathematics, John McCarthy is said to have chosen the neutral title AI because he was trying to avoid scrutiny by his peers. Among them, Norbert Wiener, said to be McCarthy’s role model, was a scientist, and linguistics expert, and likely the first researcher to work on Esperanto, who surely would have been tough to argue semantics with.

1970s | Computer prescriptions become possible

Stanford researchers develop MYCIN, one of the first rule-based systems able to quiz doctors with a series of simple questions that it can match to ~600 rules in a clinical database to help diagnose infections, and recommend antibiotics. With its diagnostics rating performing slightly better (a few percent, around 65% total acceptability) than the 5 faculty members in tests, and its ability to answer why, how, and why not something else, when quizzed, it became clear that its new partitioning algorithm that ruled out entire diagnostics trees, separated clinical fact-finding from more common, single-truth computing approaches.

# aside: MYCIN, the system’s name was derived from the word suffix of many antibiotics.

1977 | Systems dialogue: initiated

Based on the early breakthroughs of SHRDLU (1968-1970) at MIT, a system able to handle natural language reasoning in a constrained domain, in 1977 ARPA funds a 5 year study called Speech Understanding Research or SUR. This speaking dialog system is developed with the ability to process spoken input in English and translate output to several European languages. While immediate applications were found in logistics, customer service, tech support, and education, progress also pointed the way for language models with enhanced conversational capabilities, including, the medical field.

# aside: HARPY (one of the systems used) achieved 91% sentence accuracy using a vocabulary of 1000 words, which topped the expert’s program expectations.

1980s | Re: QMR Was: INTERNIST-1

Based on dialog system technology, INTERNIST-1 is developed at the University of Pittsburgh over 10 years to assist with internal medical diagnoses. INTERNIST-1, later known as Quick Medical Reference or QMR, stands apart because it does not adopt probabilistic models because its authors argue that medicine is more qualitative than quantitative. They go on to implement a rules based differential diagnostic instead of using a bayesian solution. This leads to custom logic for medical epistemology that can rule out entire decision trees, placing QMR in its own class of specialized solutions.

# aside: In not deploying Bayesian math, the QMR solution side-stepped decades of clinical resistance because of quantitative logic that would not let models easily reveal its reasoning. This is now often called “black box” AI.

1990+ | Bayesian networks resurface in clinical decision support

In a push and pull, probabilistic reasoning through Bayesian networks (BNs) gains popularity again for diagnosis and treatment planning. Its approach performs by leaps and bounds better in cardiac, cancer, and psychological conditions, but run into issues in domains with less structured data or unclear causal pathways.

# aside: Bayesian networks struggle most with subjective and overlapping symptoms like in mental health diagnostics, and benefit most from clean, consistent data structures, that are often not easy to come by.

1995+ | Modeling cause, not just correlation

As the limitations of classic Bayesian networks became clearer, researchers like Judea Pearl (Causality: Models, Reasoning, and Inference) and Peter Lucas (work in Alzheimers detection) introduce formal causal graphs and do-calculus, which reduce complex interventional questions into observable probabilities, to better medical reasoning. While Pearl’s theory gains traction, use lags because healthcare rarely produces the kind of interventional data his models require; randomized, controlled, and counter-factual.

# aside: In Healthcare “just run the experiment” often isn’t an option like it is in other industries. Ethical, logistical, and privacy limits require that causality has to be inferred, and can’t be observed.

2000s | Causal inference, applied

Following Judea Pearl’s theory, researchers begin applying do‑calculus and causal graphs to real‑world healthcare problems—especially in epidemiology and public health, where randomized trials are often impossible. These methods help estimate treatment effects and correct observational studies without relying solely on prediction. As summarized in the UCLA Causality Resources, this marked a key divergence from traditional machine learning: in medicine, it mattered whether something caused an outcome—not just if it was associated.

# aside: Causal inference gives medicine a way to ask “what if?” without experiments, but answers still depend on how much the data remembers.

2000s | When health records turn infrastructure

Widespread adoption of EHR systems across hospitals and clinics transforms healthcare data from fragmented paper charts into machine-readable digital records. EHRs create the conditions for AI to emerge: large volumes of longitudinal, structured (and semi-structured) clinical data are now accessible. The problem is that these systems aren’t built with modeling in mind. Data is spotty across vendors, unstandardized across systems, and difficult to access for researchers.

# aside: Medical records date back as long as 1,600-3,000 BC as found by archeologists in translations of Egyptian hieroglyphic papyri.

2011 | IBM Watson in Healthcare

After winning Jeopardy! (YouTube), IBM Watson technology was used in oncology to assist physicians make treatment decisions.

# aside: More then 10 years after Watson wins the quiz show, IBM sells off Watson Health after failing to turn its powerful and futuristic TV performance into clinical outcomes.

2015 | Deep learning upends imaging diagnostics

Deep learning algorithms start to outperform radiologists and dermatologists in detecting certain conditions from medical imaging—especially in fields like ophthalmology, dermatology, and oncology. These advances aren’t just about better models—they rely on software systems capable of ingesting, labeling, and scaling large volumes of annotated clinical images, often curated in partnership with specialists.

# aside: AI gets the credit, but the driver behind every query’s response is clinical data created and enriched by physicians and experts.

2018 | AI gets cleared to diagnose, solo

The FDA approves IDx-DR, an autonomous diagnostic system for detecting diabetic retinopathy in retinal images—without a clinician involved. It’s the first time an AI is allowed to make a medical decision independently, marking a regulatory milestone that shifts AI from decision support to decision maker.

# aside: Unlike earlier tools, IDx-DR didn’t assist doctors, it bypassed them. This could be a breakthrough moment that may have many physicians rethink their work and impact.

2020 | AI thrives in a crisis

AI is rapidly developed and deployed to assist with COVID-19 diagnosis, triage, treatment modeling, and vaccine discovery. Systems are often trained on fragmented, rapidly evolving datasets, many crowd-sourced from overwhelmed hospitals. While effectiveness varies, the urgency of the pandemic made powerful, but imperfect, solutions necessary in the global public health response.

# aside: In China, the startup InferVision deployed its pneumonia‑detecting algorithm to 34 hospitals (Wired), and was able to review over 32,000 cases in just a few weeks. 

2022 | Large Language Models enter the exam room

GPT-4 and similar AI models demonstrate medical knowledge that can be compared to physicians across multiple-choice licensing exams. Unlike prior systems trained on structured medical data, these models are trained on text collections scraped from the public internet—books, journals, Wikipedia, Reddit, etc with a surprisingly impact on it’s diagnostics and medical ability.

# aside: GPT-4 passed the boards without accruing medical school debt by copying everyone else’s work from the web (mozillafoundation).

2023 | AI designs medicine

Beyond simulation, generative AI starts producing drug candidates that enter clinical trials. Systems don’t just predict molecular behavior; they can generate new chemical structures based on target parameters, are trained on proprietary compound libraries and biomedical literature, and can often compress yearlong development timelines into months.

# aside: An AI-designed drug for pulmonary fibrosis reached phase II trials—just 18 months after the molecule was created. Previous drug development timelines without the tech may have taken 4-6 years to just phase I.

2025 | Systems make their own rounds

Advanced AI systems receive regulatory approval for autonomous patient monitoring and personalized care recommendations. Built on real-time inputs from wearables, health records, and symptom reporting, these assistants offer continuous oversight and proactive guidance—without needing a clinician in the loop.

# aside The U.S. Food and Drug Administration maintains the Artificial Intelligence‑Enabled Medical Devices list: a publicly updated database of all AI/ML‑powered devices authorized for marketing in the U.S. 

From here on out, every two weeks

This chronological series of events is an arc, problem to solution, its a trail of messes, then logical consequences, and experts making the best decisions they could, with the tools they had.

It’s clear that healthcare tech diverges from other domains, and not just because the stakes are higher. It’s because the system is trying to encompass the structured and the messiness; the clinical, ethical, emotional. They still need sharp focus and lots of work to quantize, simulate, understand, to output the right advice and the right time. That is the opportunity.

We’ll keep tracking that, and how we think about solutions, in the issues ahead and hope that you will follow along.

— The team at Automate.clinic

What are your thoughts? Was this rewind helpful? Was there something critical missing?

E-mail us at processing@automate.clinic and we'll credit you if your contribution makes it into the first edition.