Press Hound

Query

Find articles

Pick a keyword, optionally a second one to combine it with (and, or, either but not both, or but not), then narrow the articles down by text, source and time range. Keywords update the results right away; filters apply with their button.

Query

Keywords · results update right away
Mass General Brighamorganization
cancertopic
Filters · apply with the button

How they overlap

Of the articles on each keyword in the last 7 days, with the same text and sources, how many also mention the other. Per lean: the share of its outlets' articles on Mass General Brigham that mention cancer.

100.0% of the 1 article on Mass General Brigham also mention cancer, as do 1.7% of the 59 articles on cancer.

Left-leaning1 of 1 article
100.0%
Least biasedno article on Mass General Brigham
–
Right-leaningno article on Mass General Brigham
–

Who picked it up when

The Guardian mentioned it first (Oct 23, 2026, 3:00 AM EDT). No least biased outlet mentioned it. No right-leaning outlet mentioned it.

One row per source with a matching article, from the most left-leaning on top: a dot for each article, on a bar for each stretch of its coverage (a pause of more than 24 hours starts a new one). Hover or tap a dot for the headline, click it to read the article.

Drag across the strip of articles per day to zoom into some days.

Source lean:leftcenterright

Source leans by Media Bias/Fact Check.

Results

1 article mentions Mass General Brigham and cancer in the last 7 days (Oct 21 to Oct 27).

Articles matching the query
Article
The GuardianCan AI really predict cancer survival better than a doctor? I have my doubts | Devi SridharThe Guardian

Take a selfie to see how healthy you are: the idea is enticing and this new research is promising. But so many factors have been ignored The idea that a single selfie could predict how long someone with cancer may live sounds like an incredible breakthrough. AI is getting better at spotting patterns in medical data, but can it be used to reliably predict what will happen to an individual patient? I asked a clinical colleague what she thought about the recent headlines . She found it an interesting development given that doctors have long used facial appearance to assess patient health. For example, she noted that they may write “appears stated age” or “appears older than stated age” as a proxy for general health. Now, researchers from Mass General Brigham, a healthcare institution in Massachusetts, have taken this idea a step further. They have developed an AI model trained on more than 40 million facial images – called FAHR-Face – to assess biological age and predict survival in cancer patients. For biological age, they developed FAHR-FaceAge, which estimates age from certain facial features such as the forehead, the wrinkles between the nose and mouth and the temples. For cancer survival, they developed FAHR-FaceSurvival, trained using almost 35,000 photos of cancer patients. This model focused more closely on the area below the eyes and the bridge of the nose – regions of the face “ linked with dying ”. Dr Raymond Mak , one of the study’s authors, noted that these parts of the face are the same as those referenced by Hippocrates in 400BC as signs of muscle wasting and mortality risk: “the nose sharp, the eyes sunken, the temples fallen in”. Prof Devi Sridhar is chair of global public health at the University of Edinburgh Continue reading...

0.60

Source leans by Media Bias/Fact Check.

Other keywords in the matching articles: the share of them that mention each, with the number of articles.

No other keyword appears in more than one of the matching articles.

Coverage by source

The share of each source's stories in the last 7 days that match the query, from left to right. Sort the table to reorder the bars.

Source lean:leftcenterright

Source leans by Media Bias/Fact Check.

Articles mentioning Mass General Brigham and cancer, by source, in the last 7 days
The Guardian0.2%1609

Coverage by lean

The share of stories that match the query, in the last 7 days, averaged over the outlets of each political lean with at least 3 stories. The groups differ in size, so a single outlet weighs more in the smaller ones.

Left-leaning1 of 15 sources
<0.1%
Least biased0 of 8 sources
0.0%
Right-leaning0 of 15 sources
0.0%

Lean index −3.6: covered more by left-leaning outlets

Each outlet at its MBFC bias score, from −10 (far left) to +10 (far right), as high as the share of its stories that match the query. The lean index is the average score of the outlets covering it, weighted by that share.

lean indexour sources' average (+0.4)leftcenterright

Bias scores by Media Bias/Fact Check.

Source leans based on ratings by Media Bias/Fact Check.