This year @demishassabis predicted AI could cure all disease in a decade.

But Claus Wilke & Derek Lowe say biology is far more complex, or progress will be limited by clinical trials & economics.

In a new 4hr episode of the Hard Drugs podcast, we answer:

Will AI solve medicine and cure all diseases (within a decade)?

We talk about drug discovery, virtual cells, the Human Genome Project, manufacturing, nanobots, innovative clinical trial design, and much more.

AI is already being used in drug discovery, and there’s been a lot of progress predicting the structure of soluble proteins, tweaking proteins and designing new structures, as we’ve covered in previous episodes.

But there’s still a huge gap in understanding protein dynamics and interactions, as there are many areas where measurement tools and data collection are limited, including events that happen in the span of milliseconds or microseconds, which is how fast many things occur in biological systems.

And while computing has scaled exponentially with Moore’s Law, drug development has faced the opposite: Eroom’s Law, where innovation has gotten more complex and more expensive over time.

Even with promising drug candidates, we talk about why human testing – not in animals or virtual cells – will continue to be vital, to test which ones are effective and safe, even though models will help earlier in the pipeline.

Beyond that, large samples and long follow ups are needed to detect rare side effects, understand whether drugs cause long-term complications, and find ways to manage them. It’s hard to see AI getting around the desire for rigorous safety data in real humans.

Another big challenge is the capital and expertise needed to produce and scale personalized medicines and complex biological products, surgeries, transplants, antibodies, and gene-editing tools, which have entirely different cost structures from small molecule drugs.

Their manufacturing and delivery often require highly skilled staff and expensive, intensive, individualized procedures.

Cost challenges are also severe for tropical and rare diseases, where the financial return to diagnose, do research, develop drugs, manufacture and deliver them at scale, is limited.

Without philanthropic funding and economic growth, a lot of diseases are going to remain uncurable, and a lot of people are going to go untreated – whether that’s because of a lack of trust, poor economic and financial incentives, limited public health ambition, and policy.

In one sense, we’re skeptical that AI can solve medicine on its own. But in another, there are many areas where we think AI can help.

So the episode also functions as a roadmap to speed up medical progress and scale up the delivery of lifesaving medicines – with AI and other approaches to reform the pipeline.

What are the economic incentives, innovative trial designs, and data collection efforts that can help drive further medical progress? And how does AI fit in?

You’ll have to listen to find out!

Timestamps:

0:04:34 Contrasting AI optimism and skepticism
0:32:44 The non-linear path between science and technology
1:01:30 The fundamental need for experiments
1:23:15 Animals, organoids, and virtual cells
1:50:47 The challenges of collecting drug efficacy data in humans
2:34:02 The long road to drug safety data
3:06:09 The cost problem of delivering biological drugs and personalized medicine at scale
3:45:35 The global skew in R&D and healthcare funding
4:01:48 Trust, ambition, and the final barriers to medical progress Listen, watch or read wherever you get your podcasts!

Spotify:
spotify.link/E3ThEtYvRXb

Apple podcasts:
podcasts.apple.com/gb/podcast/wil…

In February, the US government ended some of the most important survey programs globally, used in research & policy for lower- and middle-income countries.

In a new article, I explain why the Demographic and Health Surveys were so critical and why they should be rescued. /1 Here's a link to the article:
ourworldindata.org/demographic-he…

Hey researchers, bloggers, everyone interested in fertility data!

We recently added lots of new data at Our World in Data on fertility rates, ages at childbirth, twin birth rates, birth seasonality, and more.

Here's a thread of what you can find on the site! 🧵 Our new data comes from a range of sources including the Human Fertility Database, the UN World Population Prospects, the Human Multiple Births Database, the Human Mortality Database, and more.

What does the total fertility rate mean?

It's a common misconception to think the TFR is a prediction of the average number of children women will have over their lifetimes. But it's not. 🧵 The chart shows data from Sweden, which has the longest time series:

The fertility rate fell in the late 19th & early 20th centuries, regardless of metric.

Since then, the TFR has risen and fallen. But most cohorts had 2 children by the end of their childbearing years.

New article by me!

I explore the baby boom in 7 charts, including some trends you (probably) didn't know:

1. Birth rates began to rise in the 1930s, before World War II The baby boom is typically defined as the years 1946—1964.

For example, Brittanica’s entry states that the baby boom is “the increase in the birth rate between 1946 and 1964”. Similarly, the US Census Bureau defines baby boomers as “those born between 1946 and 1964”.

What’s the most common season for babies to be born?

In many European countries, it’s late summer or autumn. Births are 5% to 10% more common in these months than others.

But this seasonal pattern used to be different. 🧵 The chart shows the most common month for births each year. Weʼve shown the birth rate per day to adjust for months that are longer or shorter.

We’ve focused on France, which has data since the 1860s.

As you can see, spring was the most common season for births then.

Many people talk about the 'Golden Age of Antibiotics' but I hadn't seen it visualized properly.

How abundant was antibiotic development during that time period? How many types of antibiotics were developed?

So I visualized it myself! Second, you may have heard that it took a long time for penicillin to be scaled up.

What about other antibiotics? How long did it take for them to be mass produced and introduced clinically, after they were discovered?

I created a visualization of this as well:

It's December, which means it's time for a retrospective on some breakthroughs in medical innovation this year.

Here are five: 🧵 1. Lenacapavir is a long-acting antiviral injection for HIV. It was initially approved to treat people with resistant HIV, and recent trials show it’s also highly effective for prevention.

New article by me!

It's possible to virtually eliminate cervical cancer worldwide with existing HPV vaccines.

This is a huge opportunity to save hundreds of thousands of women every year; tens of millions over the next decades. Let's take it!
ourworldindata.org/hpv-vaccinatio… Every year, around over half a million women are diagnosed with cervical cancer, and more than 300,000 die from the disease.

These deaths are particularly tragic because we can prevent them through vaccination and early screening.

40 years ago HIV was invariably fatal: 100% of people with HIV died from the disease typically in a few years.

Today antiretrovirals lead to near-normal life expectancy & prevent the virus's spread.

It's estimated that antiretrovirals save over a million lives each year. Recent medical advances could take this progress even further:

https://x.com/salonium/status/1803820730460586011

I read this recent 'microplastics in the brain' article and I have a lot of questions. 🧵

https://twitter.com/Douglas_Main/status/1826310290473906260

First, the reporting of this study...

The Guardian article says it's "a pre-print paper still undergoing peer review that is posted online by the National Institutes of Health"

Note the NIH has many disclaimers on their site explaining they don't endorse the content of articles

@drStuartGilmour Hi! Thanks for the comments with this. I think there's a lot of confusion here, let me try to clarify each point further. @drStuartGilmour As I describe in the article, checkboxes don’t directly feed into vital stats in other countries. Italy has enhanced surveillance to investigate potential maternal deaths; it shows their vital stats also underreport maternal deaths

epicentro.iss.it/en/itoss/mater…

https://x.com/drStuartGilmour/status/1790618465411407886

What do people die from at different ages?

I hadn’t seen a satisfying chart that showed causes of death in different age groups all at once, so I just made it myself.

Turns out, in the US, “external causes” are a majority of deaths until ~age 40
scientificdiscovery.dev/p/20-so-many-g…
In childhood, the most common cause of death are ‘external causes’.

This is a broad category (in red) that includes accidents, falls, violence & overdoses.

Also a notable contribution from birth disorders (muted green), childhood cancers (blue) & respiratory diseases (cyan).

Looking for a movie to watch this weekend?

I watched around 250 films from the “classic Hollywood era” of the 1930s-50s, while I was at university.

Screwball comedies, film noir, lots of great films still fun to watch today. Here are my favourites with one-line synopses. 🧵 First, the comedies:

You probably knew that lung cancer death rates rose and fell greatly over the 20th century.

But did you know this also affected cardiovascular diseases?

Since the 1950s, crude cardiovascular death rates have declined by almost half, in the US

If you look at age-standardized death rates, i.e. people of the same age, you'll see the decline is even larger:

a reduction of almost 75% since the 1950s:

Women live longer than men.

On average, the gap in life expectancy is 5 years globally.

But this gap isn't a constant — it varies widely around the world and has changed over time. How and why?

New article by me!
ourworldindata.org/why-do-women-l…


The sex gap in life expectancy begins at birth:

Newborn boys tend to have higher death rates than newborn girls, as they’re more vulnerable to diseases and genetic disorders.

In 2021, all countries were above the diagonal, showing that infant male death rates were higher.

This is a great scientific achievement.

But I also see it as an economic & political blunder — the world could have had a malaria vaccine sooner. We should learn from this, not just celebrate & move on. That's what this 9000 word essay is about.
worksinprogress.co/issue/why-we-d…
The malaria vaccine was trialled for the first time in humans in 1997.

It was approved in 2021.

Each step of the journey faced struggles in funding and operations, to set up & run each next stage of trials.

Some claim that flu cases were only very low during the pandemic because of a lack of testing for flu.

But this is not the case.

You can see here the *percentage of flu tests* that were positive.
ourworldindata.org/explorers/infl… So why did flu decline during the pandemic?

Social distancing had a much larger impact than one might expect, because of the mathematics of epidemics and the R number.

Flu researchers 📢

We have a new global data explorer on @OurWorldInData that includes:

• Confirmed flu cases, by strain & surveillance
• Test positivity
• Flu-like illness metrics: ILIs, ARIs, SARIs

ourworldindata.org/explorers/infl… Here are some things you can do with the explorer.

1. See the share of flu tests that were positive, in different countries.

> by week or month
> by surveillance type: all, sentinel or non-sentinel
ourworldindata.org/explorers/infl…

We have a new page on @OurWorldInData on Influenza!

• How many people die from flu each year?
• How has mortality changed over time?
• Which strains are currently circulating?
• What can flu-like illness trackers tell us?

All here + thread below!
ourworldindata.org/influenza Here are 7 things you may not know about flu, from our new page.

1. Globally, flu kills around 400,000 people from respiratory disease on average each year.

This is shown as a rate per 100,000 people aged over 65.
ourworldindata.org/influenza?insi…

You often hear that full siblings "share 50% of their DNA."

But did you know this is actually the median and the range is pretty wide, roughly between 40–60%? journals.plos.org/plosgenetics/a…
isogg.org/wiki/Autosomal… Why?

Children inherit chromosomes from each parent, but those had gone through recombination and have a mix of segments from their parents (the child's grandparent).

Siblings inherit some of the same segments and some different.