#early-detection — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #early-detection, aggregated by home.social.
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What happens when a life-altering neurological condition mimics everyday stress? 📉
Misdiagnosis in Early-Onset Parkinson's is a silent crisis. Because symptoms often manifest in patients in their 30s or 40s, standard clinical biases frequently attribute motor changes to lifestyle factors.
🔗 Links in first comment 👇
https://regenstep.com/parkinsons-disease-2026-early-onset-misdiagnosis-enzyme-therapy/
#MedicalBias #PatientAdvocacy #EarlyDetection #FunctionalNeurology #EOPD
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Stage Four Diagnosis Follows Dismissal of Early Cancer Sign
A father of three, Leon, has stage 4 kidney cancer after an early symptom was missed. Doctors give him 1-3 years to live. A GoFundMe is raising money.
#CancerDiagnosis, #KidneyCancer, #HealthNews, #EarlyDetection, #GoFundMe
https://newsletter.tf/stage-4-cancer-diagnosis-early-sign-ignored/
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Leon, a 35-year-old father, has been diagnosed with stage 4 kidney cancer. This is a serious diagnosis after an early sign was initially overlooked.
#CancerDiagnosis, #KidneyCancer, #HealthNews, #EarlyDetection, #GoFundMe
https://newsletter.tf/stage-4-cancer-diagnosis-early-sign-ignored/ -
Record Skin Cancer Cases Spur Health Warnings
Skin cancer cases are at a record high. Health officials warn people to watch for 3 key signs and see a doctor if they notice changes.
#skincancer, #healthwarning, #earlydetection, #moles, #skincare
https://newsletter.tf/skin-cancer-cases-rise-health-warnings/
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Skin cancer cases have reached a new high, with health experts urging people to be aware of three key warning signs for early detection.
#skincancer, #healthwarning, #earlydetection, #moles, #skincare
https://newsletter.tf/skin-cancer-cases-rise-health-warnings/ -
Silence isn't an option when early detection is the cure. 🎗️ We've curated the ultimate toolkit of advocacy messages to help you speak up, spread hope, and remind your circle that health is wealth. Ready to copy and paste to your feed. 🚀
#EarlyDetection #CancerAdvocacy #HealthAwareness #SaveLives #AdvocacyToolkit
Read more: https://mooddrafts.com/how-to-help-cancer-advocacy-messages-that-actually-save-lives/
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Lung Cancer Diagnoses Highlight Early Detection's Crucial Role
A 59-year-old smoker in Sussex found an 18mm growth after a lung health scan. Early detection is key for lung cancer.
#LungCancerScreening, #SussexHealth, #EarlyDetection, #SmokerHealth, #CancerAwareness
https://newsletter.tf/lung-cancer-scan-sussex-smoker-growth-found/
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Over 34,000 people in Sussex have had lung health scans. This program aims to find lung cancer early in smokers and ex-smokers.
#LungCancerScreening, #SussexHealth, #EarlyDetection, #SmokerHealth, #CancerAwareness
https://newsletter.tf/lung-cancer-scan-sussex-smoker-growth-found/ -
DATE: May 28, 2026 at 04:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: A virtual reality navigation test predicts Alzheimer’s risk in healthy adults
Struggling with spatial navigation in a virtual reality environment can predict actual brain shrinkage a year later in adults without memory problems. These navigation tests might offer a new way to spot the earliest signs of Alzheimer’s disease long before memory loss begins. The findings were recently published in the journal Alzheimer’s Research Therapy.
Alzheimer’s disease damages the brain for years before a person experiences noticeable memory decline. Some of the first brain areas to deteriorate are those responsible for spatial navigation. This is the ability to understand where you are in a given environment and how to get to your destination. Because these internal navigation centers degrade so early in the disease process, medical professionals are looking for ways to test a person’s navigation skills as a warning sign.
One specific navigation skill is called path integration. This is the brain’s ability to track a person’s current position and direction of movement by using internal cues. It relies on sensory feedback from balance, body movement, and visual flow rather than external landmarks. When you wake up in the dark and walk to the bathroom based entirely on your sense of distance and direction, you are using path integration.
When the brain networks supporting these spatial calculations begin to break down, people start making errors in their internal maps. A team of researchers wanted to see if these specific spatial errors could forecast physical changes in the brain over time. Kazuya Kawabata and Sayuri Shima, researchers at Fujita Health University in Japan, led the investigation. They worked alongside Hirohisa Watanabe and several other colleagues.
The research team set out to determine if subtle miscalculations in a virtual reality game could predict structural brain decline. They specifically wanted to study adults who currently show no signs of cognitive impairment. To answer this question, the researchers recruited 71 adults with healthy cognitive function. These participants underwent brain imaging at the beginning of the study and again about one year later.
During the initial visit, the participants also gave blood samples and completed a virtual reality navigation task. They wore a headset that placed them in a featureless circular arena designed to test spatial awareness. The virtual room was 20 virtual meters wide and bounded by blank walls to ensure participants could not rely on visual landmarks.
Using a hand-held controller for forward movement and a swivel chair for physical rotation, participants moved to two different checkpoints in the virtual room. The checkpoints were marked by colored flags. After reaching the second checkpoint, the visual markers disappeared from the virtual world. The participants then had to rely solely on their internal sense of direction to return to their original starting point.
The research team measured two types of mistakes during this return trip. The first was path integration error, which is the physical distance between where the participant stopped and the actual starting point. The second was angular error, which measured how far off their rotational direction was compared to the correct path back to the start.
The researchers then compared these behavioral errors to changes in the participants’ brain scans over the following year. They looked specifically at the thickness of the outer layer of the brain, known as the cortex, and the overall volume of different brain regions. A reduction in cortical thickness or volume indicates that brain cells are shrinking or dying off.
The results showed a clear pattern connecting virtual reality performance to structural brain health. Participants who made larger path integration errors at the start of the study experienced faster thinning and volume loss in specific parts of the brain. These physical reductions occurred in several areas, including the parahippocampal gyrus and the posterior cingulate cortex.
These specific brain regions are highly vulnerable to early damage from neurodegenerative diseases. The parahippocampal gyrus helps the brain encode new memories and process spatial locations. The posterior cingulate cortex acts as a central hub that connects memory processing to emotional regulation and spatial awareness. Experiencing tissue loss in these areas is often one of the earliest physical signs of cognitive decline.
Errors in rotational direction, or angular errors, showed a very similar relationship with brain shrinkage over the one-year period. The researchers noted that angular errors were not closely tied to the general chronological age of the participants. This suggests that rotational mistakes might be a specific indicator of disease related decline rather than a normal symptom of getting older.
The team also analyzed the baseline blood samples to look for specific proteins that act as biological markers for Alzheimer’s disease. They tested for tau proteins and glial fibrillary acidic proteins. Tau proteins can form destructive tangles inside brain cells, while glial proteins are structural components of support cells that leak into the blood when the brain is damaged.
Both the path integration errors and the angular errors were tied to higher levels of these proteins in the blood. This biological connection strongly supports the idea that the navigation mistakes reflect underlying disease processes. The distance errors proved to be highly accurate at identifying the specific individuals who experienced the fastest rate of brain thinning in the parahippocampal region.
“Our findings suggest that VR-PI performance captures both molecular (blood biomarker) and structural (MRI) signatures that emerge before overt clinical impairment,” says Dr. Kawabata. This dual connection to both blood proteins and brain imaging makes the virtual reality test a promising tool for early detection.
Despite the clear patterns, the researchers noted a few limitations to their work. While the virtual reality system requires people to physically rotate in a chair, it does not involve actual walking. This means it lacks the physical sensations of forward acceleration and leg movement that the brain normally uses for path integration. Virtual reality can only partially mimic the sensory experience of walking through the real world.
The automated software used to measure brain thickness from the magnetic resonance imaging scans can also introduce slight measurement variations. The team also mentioned that their participant group was relatively small and consisted entirely of adults in Japan. Because spatial navigation strategies can differ across cultural and educational backgrounds, the results might not perfectly apply to global populations.
Future research will need to include larger and more diverse groups of people to see if these patterns hold true across different demographics. Scientists also need to use more advanced imaging techniques to look closer at the earliest signs of brain shrinkage in these specific spatial navigation centers. The researchers hope future studies will track participants for longer than one year to see how their cognitive health changes over a longer timeline.
Still, connecting a simple behavioral test to both biological proteins and physical brain shrinkage offers a promising path forward. Testing navigation skills could eventually become a standard part of routine checkups for older adults. Spotting these problems early gives doctors a much better chance to intervene before severe memory loss takes hold.
“Our approach may allow earlier identification of risk of neurodegenerative diseases, including AD. Over the longer term, it may contribute to a shift toward earlier detection, potentially enabling timely therapeutic interventions at preclinical stages and delaying disease progression, thereby preserving cognitive function and quality of life,” concludes Dr. Kawabata.
The study, “VR-based path integration predicts individual risk of rapid cortical decline: a one-year longitudinal study in cognitively unimpaired adults,” was authored by Kazuya Kawabata, Sayuri Shima, Reiko Ohdake, Epifanio Bagarinao, Yasuaki Mizutani, Harutsugu Tatebe, Riki Koike, Atsushi Kasai, Akihiro Ueda, Mizuki Ito, Junichi Hata, Shinsuke Ishigaki, Hiroshi Toyama, Takahiko Tokuda, Akihiko Takashima, and Hirohisa Watanabe.
-------------------------------------------------
DAILY EMAIL DIGEST: Email [email protected] -- no subject or message needed.
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
NYU Information for Practice puts out 400-500 good quality health-related research posts per week but its too much for many people, so that bot is limited to just subscribers. You can read it or subscribe at @PsychResearchBot
Since 1991 The National Psychologist has focused on keeping practicing psychologists current with news, information and items of interest. Check them out for more free articles, resources, and subscription information: https://www.nationalpsychologist.com
EMAIL DAILY DIGEST OF RSS FEEDS -- SUBSCRIBE: http://subscribe-article-digests.clinicians-exchange.org
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It's primitive... but it works... mostly...
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #VRpathintegration #AlzheimersPrediction #spatialnavigation #corticalthinning #neurodegeneration #bloodbiomarkers #tauproteins #VRinmedicine #earlydetection #cognitivehealth
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DATE: May 28, 2026 at 04:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: A virtual reality navigation test predicts Alzheimer’s risk in healthy adults
Struggling with spatial navigation in a virtual reality environment can predict actual brain shrinkage a year later in adults without memory problems. These navigation tests might offer a new way to spot the earliest signs of Alzheimer’s disease long before memory loss begins. The findings were recently published in the journal Alzheimer’s Research Therapy.
Alzheimer’s disease damages the brain for years before a person experiences noticeable memory decline. Some of the first brain areas to deteriorate are those responsible for spatial navigation. This is the ability to understand where you are in a given environment and how to get to your destination. Because these internal navigation centers degrade so early in the disease process, medical professionals are looking for ways to test a person’s navigation skills as a warning sign.
One specific navigation skill is called path integration. This is the brain’s ability to track a person’s current position and direction of movement by using internal cues. It relies on sensory feedback from balance, body movement, and visual flow rather than external landmarks. When you wake up in the dark and walk to the bathroom based entirely on your sense of distance and direction, you are using path integration.
When the brain networks supporting these spatial calculations begin to break down, people start making errors in their internal maps. A team of researchers wanted to see if these specific spatial errors could forecast physical changes in the brain over time. Kazuya Kawabata and Sayuri Shima, researchers at Fujita Health University in Japan, led the investigation. They worked alongside Hirohisa Watanabe and several other colleagues.
The research team set out to determine if subtle miscalculations in a virtual reality game could predict structural brain decline. They specifically wanted to study adults who currently show no signs of cognitive impairment. To answer this question, the researchers recruited 71 adults with healthy cognitive function. These participants underwent brain imaging at the beginning of the study and again about one year later.
During the initial visit, the participants also gave blood samples and completed a virtual reality navigation task. They wore a headset that placed them in a featureless circular arena designed to test spatial awareness. The virtual room was 20 virtual meters wide and bounded by blank walls to ensure participants could not rely on visual landmarks.
Using a hand-held controller for forward movement and a swivel chair for physical rotation, participants moved to two different checkpoints in the virtual room. The checkpoints were marked by colored flags. After reaching the second checkpoint, the visual markers disappeared from the virtual world. The participants then had to rely solely on their internal sense of direction to return to their original starting point.
The research team measured two types of mistakes during this return trip. The first was path integration error, which is the physical distance between where the participant stopped and the actual starting point. The second was angular error, which measured how far off their rotational direction was compared to the correct path back to the start.
The researchers then compared these behavioral errors to changes in the participants’ brain scans over the following year. They looked specifically at the thickness of the outer layer of the brain, known as the cortex, and the overall volume of different brain regions. A reduction in cortical thickness or volume indicates that brain cells are shrinking or dying off.
The results showed a clear pattern connecting virtual reality performance to structural brain health. Participants who made larger path integration errors at the start of the study experienced faster thinning and volume loss in specific parts of the brain. These physical reductions occurred in several areas, including the parahippocampal gyrus and the posterior cingulate cortex.
These specific brain regions are highly vulnerable to early damage from neurodegenerative diseases. The parahippocampal gyrus helps the brain encode new memories and process spatial locations. The posterior cingulate cortex acts as a central hub that connects memory processing to emotional regulation and spatial awareness. Experiencing tissue loss in these areas is often one of the earliest physical signs of cognitive decline.
Errors in rotational direction, or angular errors, showed a very similar relationship with brain shrinkage over the one-year period. The researchers noted that angular errors were not closely tied to the general chronological age of the participants. This suggests that rotational mistakes might be a specific indicator of disease related decline rather than a normal symptom of getting older.
The team also analyzed the baseline blood samples to look for specific proteins that act as biological markers for Alzheimer’s disease. They tested for tau proteins and glial fibrillary acidic proteins. Tau proteins can form destructive tangles inside brain cells, while glial proteins are structural components of support cells that leak into the blood when the brain is damaged.
Both the path integration errors and the angular errors were tied to higher levels of these proteins in the blood. This biological connection strongly supports the idea that the navigation mistakes reflect underlying disease processes. The distance errors proved to be highly accurate at identifying the specific individuals who experienced the fastest rate of brain thinning in the parahippocampal region.
“Our findings suggest that VR-PI performance captures both molecular (blood biomarker) and structural (MRI) signatures that emerge before overt clinical impairment,” says Dr. Kawabata. This dual connection to both blood proteins and brain imaging makes the virtual reality test a promising tool for early detection.
Despite the clear patterns, the researchers noted a few limitations to their work. While the virtual reality system requires people to physically rotate in a chair, it does not involve actual walking. This means it lacks the physical sensations of forward acceleration and leg movement that the brain normally uses for path integration. Virtual reality can only partially mimic the sensory experience of walking through the real world.
The automated software used to measure brain thickness from the magnetic resonance imaging scans can also introduce slight measurement variations. The team also mentioned that their participant group was relatively small and consisted entirely of adults in Japan. Because spatial navigation strategies can differ across cultural and educational backgrounds, the results might not perfectly apply to global populations.
Future research will need to include larger and more diverse groups of people to see if these patterns hold true across different demographics. Scientists also need to use more advanced imaging techniques to look closer at the earliest signs of brain shrinkage in these specific spatial navigation centers. The researchers hope future studies will track participants for longer than one year to see how their cognitive health changes over a longer timeline.
Still, connecting a simple behavioral test to both biological proteins and physical brain shrinkage offers a promising path forward. Testing navigation skills could eventually become a standard part of routine checkups for older adults. Spotting these problems early gives doctors a much better chance to intervene before severe memory loss takes hold.
“Our approach may allow earlier identification of risk of neurodegenerative diseases, including AD. Over the longer term, it may contribute to a shift toward earlier detection, potentially enabling timely therapeutic interventions at preclinical stages and delaying disease progression, thereby preserving cognitive function and quality of life,” concludes Dr. Kawabata.
The study, “VR-based path integration predicts individual risk of rapid cortical decline: a one-year longitudinal study in cognitively unimpaired adults,” was authored by Kazuya Kawabata, Sayuri Shima, Reiko Ohdake, Epifanio Bagarinao, Yasuaki Mizutani, Harutsugu Tatebe, Riki Koike, Atsushi Kasai, Akihiro Ueda, Mizuki Ito, Junichi Hata, Shinsuke Ishigaki, Hiroshi Toyama, Takahiko Tokuda, Akihiko Takashima, and Hirohisa Watanabe.
-------------------------------------------------
DAILY EMAIL DIGEST: Email [email protected] -- no subject or message needed.
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
NYU Information for Practice puts out 400-500 good quality health-related research posts per week but its too much for many people, so that bot is limited to just subscribers. You can read it or subscribe at @PsychResearchBot
Since 1991 The National Psychologist has focused on keeping practicing psychologists current with news, information and items of interest. Check them out for more free articles, resources, and subscription information: https://www.nationalpsychologist.com
EMAIL DAILY DIGEST OF RSS FEEDS -- SUBSCRIBE: http://subscribe-article-digests.clinicians-exchange.org
READ ONLINE: http://read-the-rss-mega-archive.clinicians-exchange.org
It's primitive... but it works... mostly...
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #VRpathintegration #AlzheimersPrediction #spatialnavigation #corticalthinning #neurodegeneration #bloodbiomarkers #tauproteins #VRinmedicine #earlydetection #cognitivehealth
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DATE: May 21, 2026 at 04:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: A new AI tool spots hidden signs of adult ADHD months before a formal diagnosis
Scientists have developed a new artificial intelligence tool that can predict whether an adult has attention-deficit hyperactivity disorder by looking at their past medical records. The predictive model suggests that subtle patterns in everyday healthcare visits can identify undiagnosed individuals months before a doctor formally spots the condition. This research was recently published in the journal European Psychiatry.
Attention-deficit hyperactivity disorder is a common neurodevelopmental condition that affects roughly 5 to 7.2 percent of children and about 2.5 percent of adults globally. People with this condition experience varying degrees of inattention, hyperactivity, and impulsivity that interfere with daily life. Getting a proper diagnosis as an adult tends to be quite difficult.
Doctors often struggle to identify the condition in older patients because the symptoms frequently overlap with other mental health challenges. When a diagnosis is delayed, individuals often experience academic or work impairments, increased accident rates, and a lower overall quality of life. An earlier diagnosis provides evidence-based opportunities for pharmacological treatment and therapy, which helps prevent many of these negative outcomes.
Artificial intelligence has recently shown promise in helping doctors spot hidden patterns in patient data. Many previous attempts to use machine learning to detect attention-deficit hyperactivity disorder have mostly relied on brain scans, structured behavioral assessments, or specialized physiological tests. These types of medical data are expensive and not routinely collected for the average patient.
To create a more practical tool, the researchers decided to focus on electronic health records. These records are the standard digital files that clinics and hospitals already maintain for every patient. By training a computer program to read standard medical histories, the authors hoped to create a cost-effective screening method that relies purely on information doctors already have on hand.
The scientists analyzed historical medical data from a regional healthcare system in southwestern Sweden. The database included information from primary care clinics, specialist visits, and hospital admissions between 2011 and 2022. They gathered detailed data on patient demographics, specific medical diagnoses, clinical procedures, and prescribed medications.
To build their model, the researchers started with a group of 3,570 adults who had been formally diagnosed with attention-deficit hyperactivity disorder or prescribed related medications. They also selected a control group of adults who had visited psychiatric outpatient clinics but did not have the disorder. During the design phase, the predictive model struggled to tell the two groups apart when the control group included patients with depression or anxiety.
To fix this issue, the researchers removed individuals with depression and anxiety from the control group. Because the cognitive and behavioral symptoms of depression and anxiety overlap heavily with attention issues, removing them allowed the computer to focus on the unique signatures of attention-deficit hyperactivity disorder. This adjustment left a final control group of 5,126 adults, which still provided plenty of data for the program.
The authors then fed this data into a machine learning system based on a “transformer” architecture. A transformer is a sophisticated type of artificial intelligence technology that excels at understanding sequences of information. Instead of reading words in a sentence, this specific transformer was trained to read the sequence of a patient’s medical visits and prescription codes over time.
These models use a mathematical technique called positional encoding to understand the exact chronological order of events. This allows the system to grasp how a patient’s health trajectory changes over the course of several months or years. The researchers tested whether the model could predict a diagnosis six, twelve, and eighteen months before the actual diagnosis date.
They evaluated the final model on an entirely separate set of 800 patients, splitting this test group evenly with 400 diagnosed individuals and 400 individuals without the condition. Testing the model on a separate group ensures that the artificial intelligence is evaluated on fresh information it has never seen before. The findings suggest that the model can successfully predict adult attention-deficit hyperactivity disorder using routine clinical data.
The artificial intelligence performed best when predicting a diagnosis six months in advance. At this six-month mark, the model correctly identified 80 percent of the patients who actually had the disorder. It also correctly ruled out the condition in 77 percent of the patients who did not have it.
The model achieved a score of 0.79 on a mathematical metric called the Area Under the Curve. This metric evaluates how well a predictive model distinguishes between two groups, with a score of 1.0 being perfect and 0.5 being no better than a random guess. The results remained fairly stable even when predicting diagnoses eighteen months into the past.
The scientists also examined which specific medical codes the computer used to make its predictions. To do this, they used an analytical technique called Shapley Additive Explanations. This method helps open the “black box” of artificial intelligence by showing exactly which demographic factors or clinical codes increase or reduce the predicted risk.
The analysis revealed that previous diagnoses related to substance use were strong indicators of a future attention-deficit hyperactivity disorder diagnosis. For example, medical codes indicating the use of stimulants, including heavy caffeine use, were highly predictive. The model also flagged codes related to specific blood alcohol levels ranging from 0.60 to 0.79 milligrams per 100 milliliters.
These findings align with previous research, which indicates that adults with undiagnosed attention-related issues sometimes try to self-medicate with caffeine, alcohol, or other substances. The computer program also picked up on medical codes related to childbirth complications. The data suggests that mothers who experience issues such as obstructed labor or abnormal fetal positions have a slightly higher chance of a later attention-deficit hyperactivity disorder diagnosis.
Researchers suspect this reflects broader physical and psychosocial challenges rather than a direct physical cause. Additionally, the scientists noticed distinct demographic and healthcare utilization patterns between the two groups. The diagnosed individuals tended to be younger, averaging around 31 years old compared to 52 years old in the control group.
They also had significantly more primary care and psychiatrist visits than the control group, but fewer hospital admissions and shorter hospital stays. While these findings are promising, the authors caution against viewing this artificial intelligence as a replacement for human doctors. The tool is not designed to formally diagnose anyone on its own.
Instead, it is meant to act as an early warning system that operates quietly in the background of a hospital’s computer network. By flagging patients who exhibit suspicious patterns of healthcare use, the system can simply notify doctors that a specific person might benefit from a comprehensive psychological evaluation. A trained healthcare professional must still sit down with the patient to conduct structured interviews and confirm the diagnosis.
One limitation of the study is the exclusion of patients with depression and anxiety from the control group. In a real clinical setting, doctors frequently need to distinguish between attention-deficit hyperactivity disorder and depression. Because the model was not trained on patients with these specific overlapping conditions, it might face challenges when deployed in a general psychiatric population.
The researchers also noted a slight discrepancy in how the model treated men and women. The artificial intelligence successfully identified the condition in 75.2 percent of the female patients, but only caught 66.7 percent of the male cases. The false positive rate remained consistent across genders, but the disparity in successful identification highlights the need for further evaluation to ensure equitable performance.
Moving forward, scientists hope to test this model in different healthcare systems outside of Sweden. Medical coding practices can vary significantly from one country to another, so the algorithm must prove its adaptability. The authors also suggest exploring how this data-driven approach might align with newer, more flexible ways of classifying mental health conditions in the future.
The study, “Early detection of adults ADHD using electronic health records: A machine learning study“, was authored by Omar Hamed, Farzaneh Etminani, Peter Jacobsson, and Thomas Davidsson.
-------------------------------------------------
DAILY EMAIL DIGEST: Email [email protected] -- no subject or message needed.
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
NYU Information for Practice puts out 400-500 good quality health-related research posts per week but its too much for many people, so that bot is limited to just subscribers. You can read it or subscribe at @PsychResearchBot
Since 1991 The National Psychologist has focused on keeping practicing psychologists current with news, information and items of interest. Check them out for more free articles, resources, and subscription information: https://www.nationalpsychologist.com
EMAIL DAILY DIGEST OF RSS FEEDS -- SUBSCRIBE: http://subscribe-article-digests.clinicians-exchange.org
READ ONLINE: http://read-the-rss-mega-archive.clinicians-exchange.org
It's primitive... but it works... mostly...
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #ADHD #AdultADHD #MedicineAI #ElectronicHealthRecords #MentalHealthTech #AIinHealthcare #EarlyDetection #NeurodevelopmentalDisorders #HealthTech #ADHDResearch
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DATE: May 21, 2026 at 04:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: A new AI tool spots hidden signs of adult ADHD months before a formal diagnosis
Scientists have developed a new artificial intelligence tool that can predict whether an adult has attention-deficit hyperactivity disorder by looking at their past medical records. The predictive model suggests that subtle patterns in everyday healthcare visits can identify undiagnosed individuals months before a doctor formally spots the condition. This research was recently published in the journal European Psychiatry.
Attention-deficit hyperactivity disorder is a common neurodevelopmental condition that affects roughly 5 to 7.2 percent of children and about 2.5 percent of adults globally. People with this condition experience varying degrees of inattention, hyperactivity, and impulsivity that interfere with daily life. Getting a proper diagnosis as an adult tends to be quite difficult.
Doctors often struggle to identify the condition in older patients because the symptoms frequently overlap with other mental health challenges. When a diagnosis is delayed, individuals often experience academic or work impairments, increased accident rates, and a lower overall quality of life. An earlier diagnosis provides evidence-based opportunities for pharmacological treatment and therapy, which helps prevent many of these negative outcomes.
Artificial intelligence has recently shown promise in helping doctors spot hidden patterns in patient data. Many previous attempts to use machine learning to detect attention-deficit hyperactivity disorder have mostly relied on brain scans, structured behavioral assessments, or specialized physiological tests. These types of medical data are expensive and not routinely collected for the average patient.
To create a more practical tool, the researchers decided to focus on electronic health records. These records are the standard digital files that clinics and hospitals already maintain for every patient. By training a computer program to read standard medical histories, the authors hoped to create a cost-effective screening method that relies purely on information doctors already have on hand.
The scientists analyzed historical medical data from a regional healthcare system in southwestern Sweden. The database included information from primary care clinics, specialist visits, and hospital admissions between 2011 and 2022. They gathered detailed data on patient demographics, specific medical diagnoses, clinical procedures, and prescribed medications.
To build their model, the researchers started with a group of 3,570 adults who had been formally diagnosed with attention-deficit hyperactivity disorder or prescribed related medications. They also selected a control group of adults who had visited psychiatric outpatient clinics but did not have the disorder. During the design phase, the predictive model struggled to tell the two groups apart when the control group included patients with depression or anxiety.
To fix this issue, the researchers removed individuals with depression and anxiety from the control group. Because the cognitive and behavioral symptoms of depression and anxiety overlap heavily with attention issues, removing them allowed the computer to focus on the unique signatures of attention-deficit hyperactivity disorder. This adjustment left a final control group of 5,126 adults, which still provided plenty of data for the program.
The authors then fed this data into a machine learning system based on a “transformer” architecture. A transformer is a sophisticated type of artificial intelligence technology that excels at understanding sequences of information. Instead of reading words in a sentence, this specific transformer was trained to read the sequence of a patient’s medical visits and prescription codes over time.
These models use a mathematical technique called positional encoding to understand the exact chronological order of events. This allows the system to grasp how a patient’s health trajectory changes over the course of several months or years. The researchers tested whether the model could predict a diagnosis six, twelve, and eighteen months before the actual diagnosis date.
They evaluated the final model on an entirely separate set of 800 patients, splitting this test group evenly with 400 diagnosed individuals and 400 individuals without the condition. Testing the model on a separate group ensures that the artificial intelligence is evaluated on fresh information it has never seen before. The findings suggest that the model can successfully predict adult attention-deficit hyperactivity disorder using routine clinical data.
The artificial intelligence performed best when predicting a diagnosis six months in advance. At this six-month mark, the model correctly identified 80 percent of the patients who actually had the disorder. It also correctly ruled out the condition in 77 percent of the patients who did not have it.
The model achieved a score of 0.79 on a mathematical metric called the Area Under the Curve. This metric evaluates how well a predictive model distinguishes between two groups, with a score of 1.0 being perfect and 0.5 being no better than a random guess. The results remained fairly stable even when predicting diagnoses eighteen months into the past.
The scientists also examined which specific medical codes the computer used to make its predictions. To do this, they used an analytical technique called Shapley Additive Explanations. This method helps open the “black box” of artificial intelligence by showing exactly which demographic factors or clinical codes increase or reduce the predicted risk.
The analysis revealed that previous diagnoses related to substance use were strong indicators of a future attention-deficit hyperactivity disorder diagnosis. For example, medical codes indicating the use of stimulants, including heavy caffeine use, were highly predictive. The model also flagged codes related to specific blood alcohol levels ranging from 0.60 to 0.79 milligrams per 100 milliliters.
These findings align with previous research, which indicates that adults with undiagnosed attention-related issues sometimes try to self-medicate with caffeine, alcohol, or other substances. The computer program also picked up on medical codes related to childbirth complications. The data suggests that mothers who experience issues such as obstructed labor or abnormal fetal positions have a slightly higher chance of a later attention-deficit hyperactivity disorder diagnosis.
Researchers suspect this reflects broader physical and psychosocial challenges rather than a direct physical cause. Additionally, the scientists noticed distinct demographic and healthcare utilization patterns between the two groups. The diagnosed individuals tended to be younger, averaging around 31 years old compared to 52 years old in the control group.
They also had significantly more primary care and psychiatrist visits than the control group, but fewer hospital admissions and shorter hospital stays. While these findings are promising, the authors caution against viewing this artificial intelligence as a replacement for human doctors. The tool is not designed to formally diagnose anyone on its own.
Instead, it is meant to act as an early warning system that operates quietly in the background of a hospital’s computer network. By flagging patients who exhibit suspicious patterns of healthcare use, the system can simply notify doctors that a specific person might benefit from a comprehensive psychological evaluation. A trained healthcare professional must still sit down with the patient to conduct structured interviews and confirm the diagnosis.
One limitation of the study is the exclusion of patients with depression and anxiety from the control group. In a real clinical setting, doctors frequently need to distinguish between attention-deficit hyperactivity disorder and depression. Because the model was not trained on patients with these specific overlapping conditions, it might face challenges when deployed in a general psychiatric population.
The researchers also noted a slight discrepancy in how the model treated men and women. The artificial intelligence successfully identified the condition in 75.2 percent of the female patients, but only caught 66.7 percent of the male cases. The false positive rate remained consistent across genders, but the disparity in successful identification highlights the need for further evaluation to ensure equitable performance.
Moving forward, scientists hope to test this model in different healthcare systems outside of Sweden. Medical coding practices can vary significantly from one country to another, so the algorithm must prove its adaptability. The authors also suggest exploring how this data-driven approach might align with newer, more flexible ways of classifying mental health conditions in the future.
The study, “Early detection of adults ADHD using electronic health records: A machine learning study“, was authored by Omar Hamed, Farzaneh Etminani, Peter Jacobsson, and Thomas Davidsson.
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #ADHD #AdultADHD #MedicineAI #ElectronicHealthRecords #MentalHealthTech #AIinHealthcare #EarlyDetection #NeurodevelopmentalDisorders #HealthTech #ADHDResearch
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Early Detection of Thyroid Cancer in Women: Symptoms, Risks, and Treatments
📰 Original title: The Hidden Condition Many Women Mistake For Stress, Hormones, Or Burnout
🤖 IA: It's clickbait ⚠️
👥 Users: It's clickbait ⚠️View full AI summary: https://en.killbait.com/early-detection-of-thyroid-cancer-in-women-symptoms-risks-and-treatments.html?utm_source=mastodon_world&utm_medium=social&utm_campaign=killbait.mastodon_world
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Early Detection of Thyroid Cancer in Women: Symptoms, Risks, and Treatments
📰 Original title: The Hidden Condition Many Women Mistake For Stress, Hormones, Or Burnout
🤖 IA: It's clickbait ⚠️
👥 Users: It's clickbait ⚠️View full AI summary: https://en.killbait.com/early-detection-of-thyroid-cancer-in-women-symptoms-risks-and-treatments.html?utm_source=mastodon_social&utm_medium=social&utm_campaign=killbait.mastodon_social
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Phishing Attacks Expose Gaps in Early Detection
In just 40 seconds, ANY.RUN's interactive sandbox exposed the full attack chain of a phishing attack, revealing redirects, fake pages, and signs of possible remote access. This game-changing tool helps teams detect phishing threats early, providing concrete evidence of business exposure before it's too late.
#PhishingAttacks #EarlyDetection #InteractiveSandbox #ThreatDetection #EmergingThreats
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Early-Onset Bowel Cancer: The Cost of Dismissing Symptoms
Are you ignoring bowel cancer symptoms? Learn why persistent fatigue and bowel changes in your 30s require a doctor visit as of May 2026.
#bowelcancer, #earlydetection, #healthawareness, #cancerprevention, #medicaladvice
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Medical reports from May 2026 show more people under 50 are getting bowel cancer. This is a rise compared to previous years, often caused by ignoring early signs.
#bowelcancer, #earlydetection, #healthawareness, #cancerprevention, #medicaladvice
https://newsletter.tf/early-bowel-cancer-symptoms-under-50/ -
DATE: May 14, 2026 at 04:05AM
SOURCE: SOCIALPSYCHOLOGY.ORGTITLE: Your "Um" and Pauses Could Reveal Early Dementia Risk
Source: Science Daily - Top Health
The little pauses, "ums," and moments when you struggle to find the right word may reveal far more about your brain than anyone realized. New research suggests they are tied to mental systems powering memory, planning, and attention. By using AI to analyze conversations, it's possible to predict cognitive performance with surprising accuracy, potentially allowing simple speech-based tools to detect early signs of dementia long before traditional...
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #DementiaRisk #SpeechAnalysis #CognitiveHealth #MildCognitiveImpairment #BrainHealth #UmsAndPauses #EarlyDetection #AIForHealth #MemoryPlanningAttention #VoiceBiomarkers
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DATE: May 14, 2026 at 04:05AM
SOURCE: SOCIALPSYCHOLOGY.ORGTITLE: Your "Um" and Pauses Could Reveal Early Dementia Risk
Source: Science Daily - Top Health
The little pauses, "ums," and moments when you struggle to find the right word may reveal far more about your brain than anyone realized. New research suggests they are tied to mental systems powering memory, planning, and attention. By using AI to analyze conversations, it's possible to predict cognitive performance with surprising accuracy, potentially allowing simple speech-based tools to detect early signs of dementia long before traditional...
-------------------------------------------------
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Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
NYU Information for Practice puts out 400-500 good quality health-related research posts per week but its too much for many people, so that bot is limited to just subscribers. You can read it or subscribe at @PsychResearchBot
Since 1991 The National Psychologist has focused on keeping practicing psychologists current with news, information and items of interest. Check them out for more free articles, resources, and subscription information: https://www.nationalpsychologist.com
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-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #DementiaRisk #SpeechAnalysis #CognitiveHealth #MildCognitiveImpairment #BrainHealth #UmsAndPauses #EarlyDetection #AIForHealth #MemoryPlanningAttention #VoiceBiomarkers
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Using the body’s own sounds to diagnose Alzheimer’s and Parkinson’s before the first symptoms appear
#Alzheimer #Parkinson #Health #NeurodegenerativeDisease #MedicalResearch #Healthcare #BrainHealth #Neurology #EarlyDetection #HealthInnovation #Aging
https://the-14.com/using-the-bodys-own-sounds-to-diagnose-alzheimers-and-parkinsons-before-the-first-symptoms-appear/ -
Using the body’s own sounds to diagnose Alzheimer’s and Parkinson’s before the first symptoms appear
#Alzheimer #Parkinson #Health #NeurodegenerativeDisease #MedicalResearch #Healthcare #BrainHealth #Neurology #EarlyDetection #HealthInnovation #Aging
https://the-14.com/using-the-bodys-own-sounds-to-diagnose-alzheimers-and-parkinsons-before-the-first-symptoms-appear/ -
STEPHEN GARCIA FACES STAGE 4 CANCER BATTLE
Former Gamecocks QB Stephen Garcia announced his Stage 4 colorectal cancer diagnosis on Wednesday, May 6th. He urges early detection.
#StephenGarcia, #ColorectalCancer, #Gamecocks, #EarlyDetection, #CancerAwareness
https://newsletter.tf/stephen-garcia-stage-4-cancer-diagnosis/
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Stephen Garcia, 38, has been diagnosed with Stage 4 colorectal cancer. This is a serious diagnosis that requires immediate attention.
#StephenGarcia, #ColorectalCancer, #Gamecocks, #EarlyDetection, #CancerAwareness
https://newsletter.tf/stephen-garcia-stage-4-cancer-diagnosis/ -
Stage Matters: Why When Breast Cancer Is Found Determines Survival
By Cliff Potts, CSO, and Editor-in-Chief of WPS News
Baybay City, Leyte, Philippines — May 5, 2026
Breast cancer survival is not a mystery. It is not random. In the Philippines, the single strongest factor that determines whether a woman lives or dies from breast cancer is the stage at which the disease is found.
Early-stage breast cancer is often treatable. Late-stage breast cancer is far harder to control. The difference between the two is time—time that is frequently lost because symptoms are subtle, screening is delayed, or access to diagnostic care is limited.
This is not an abstract medical concept. It is the core reality shaping outcomes across the Philippine archipelago.
What “Stage” Really Means
Cancer staging describes how far the disease has progressed. In simple terms, it answers three questions: how large the tumor is, whether cancer has spread to nearby lymph nodes, and whether it has reached other organs.
Early stages—often called Stage I or Stage II—mean the cancer is still localized or only minimally spread. At these stages, treatment options are broader, recovery is more likely, and long-term survival rates are significantly higher.
Later stages—Stage III and especially Stage IV—mean the cancer has spread more extensively. Treatment becomes more aggressive, more expensive, and less predictable. At that point, care often shifts from cure to control.
In the Philippines, too many women first enter the healthcare system at these later stages.
Survival Depends on Timing
International and regional data consistently show that breast cancer detected early has a far higher survival rate than breast cancer detected late. When found early, many women live long, productive lives after treatment. When found late, survival drops sharply.
This gap is not caused by biology alone. It is caused by delayed diagnosis.
Many women do not seek medical care because they feel no pain. Others delay because the lump seems small, because they are busy caring for others, or because they fear the cost of treatment more than the disease itself. Some simply do not have access to screening services close to home.
By the time symptoms become impossible to ignore, the disease has often already advanced.
Common Myths That Delay Care
Several persistent myths contribute to late detection in the Philippines.
One is the belief that breast cancer only affects older women. In reality, Filipino women are often diagnosed at younger ages compared to women in many high-income countries.
Another myth is that a family history is required. While family history increases risk, many women diagnosed with breast cancer have no known genetic link.
There is also the belief that cancer must hurt to be dangerous. Early breast cancer often causes no pain at all. Waiting for pain is waiting for progression.
These beliefs are understandable—but they are costly.
The Cost of Late Diagnosis
Late-stage treatment places enormous strain on families and the healthcare system. It requires longer hospital stays, more complex therapies, and greater financial sacrifice. For many households, the economic impact is devastating.
Early detection does not eliminate hardship, but it significantly reduces it. Treatment is often simpler, outcomes are better, and families retain more control over decisions.
This is why staging matters so much. It is not just a medical label. It is a dividing line between possibility and limitation.
A Preventable Pattern
The high rate of late-stage diagnosis in the Philippines is not inevitable. It reflects gaps in screening access, health education, and early referral pathways. These are solvable problems.
Improving early detection means normalizing screening, reducing fear around diagnosis, and making services accessible beyond major urban centers. It means shifting the national conversation from treatment alone to timing.
Breast cancer does not become deadly overnight. It becomes deadly when it is allowed to grow unnoticed.
Finding it earlier saves lives. That is not hope. It is evidence.
For more social commentary, please see Occupy 2.5 at https://Occupy25.com
#BreastCancer #cancerStaging #cancerSurvival #earlyDetection #medicalAwareness #PhilippinesHealth #publicHealthPhilippines #womenSHealth -
24 doctor visits. 12 days to live. 💔
Nigel’s story is a heavy reminder of why pancreatic cancer is a silent killer.
Read the signs we miss 🩺✨
https://www.karmactive.com/nigel-williams-pancreatic-cancer-24-doctor-visits-missed-diagnosis-uk/
Follow @karmactive 🔔for more updates!
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Research Links Early Smell Loss to Alzheimer's Disease
📰 Original title: Study Reveals Loss Of Smell May Be An Early Warning Sign Of Alzheimer's
🤖 IA: It's clickbait ⚠️
👥 Usuarios: It's clickbait ⚠️View full AI summary: https://killbait.com/en/research-links-early-smell-loss-to-alzheimers-disease/?redirpost=697c2db0-e1ba-497c-ae52-d5c8081dfa8c
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Research Links Early Smell Loss to Alzheimer's Disease
📰 Original title: Study Reveals Loss Of Smell May Be An Early Warning Sign Of Alzheimer's
🤖 IA: It's clickbait ⚠️
👥 Usuarios: It's clickbait ⚠️View full AI summary: https://killbait.com/en/research-links-early-smell-loss-to-alzheimers-disease/?redirpost=697c2db0-e1ba-497c-ae52-d5c8081dfa8c
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AFL Figures Confronting Serious Health Battles, Urging Public Vigilance
AFL players like Sam Docherty and Brad Johnson reveal their cancer fights, encouraging men to be aware and self-examine for early detection.
#AFLHealth, #MensHealth, #CancerAwareness, #EarlyDetection, #SamDocherty
https://newsletter.tf/afl-stars-cancer-battles-men-health-vigilance/
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Several AFL stars have recently shared their serious health battles with cancer, including testicular and brain tumors. This is a wake-up call for men's health awareness.
#AFLHealth, #MensHealth, #CancerAwareness, #EarlyDetection, #SamDocherty
https://newsletter.tf/afl-stars-cancer-battles-men-health-vigilance/ -
Fatty Liver Disease: The Silent Risk That Can Progress to Liver Failure
#TycoonWorld #FattyLiverDisease #MASLD #LiverHealth #SilentDisease #LiverFailure #LiverCare #ChronicLiverDisease #Cirrhosis #LiverCancer #LiverTransplant #DiabetesRisk #ObesityHealth #MetabolicSyndrome #HealthyLiver #PreventLiverDisease #EarlyDetection #HealthAwareness #IndiaHealth #NashikHealth #HealthcareIndia #LiverScreening #DoctorAdvice #WellnessIndia #PublicHealth #MedicalAwareness
https://tycoonworld.in/fatty-liver-disease-the-silent-risk-that-can-progress-to-liver-failure/
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Fatty Liver Disease: The Silent Risk That Can Progress to Liver Failure
#TycoonWorld #FattyLiverDisease #MASLD #LiverHealth #SilentDisease #LiverFailure #LiverCare #ChronicLiverDisease #Cirrhosis #LiverCancer #LiverTransplant #DiabetesRisk #ObesityHealth #MetabolicSyndrome #HealthyLiver #PreventLiverDisease #EarlyDetection #HealthAwareness #IndiaHealth #NashikHealth #HealthcareIndia #LiverScreening #DoctorAdvice #WellnessIndia #PublicHealth #MedicalAwareness
https://tycoonworld.in/fatty-liver-disease-the-silent-risk-that-can-progress-to-liver-failure/
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Soccer Legend Wambach Points to Early Screening After Colon Cancer Scare
Soccer star Abby Wambach credits her colon cancer screening at age 35 for saving her life, urging others to consider early checks.
#AbbyWambach, #ColonCancerScreening, #EarlyDetection, #CancerAwareness, #HealthTips
https://newsletter.tf/abby-wambach-colon-cancer-screening-age-35/
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When everyday tasks become harder: Early clues to Alzheimer’s disease
#Health #CognitiveHealth #Alzheimers #BrainHealth #Aging #DementiaAwareness #MemoryLoss #MentalHealth #Canada #EarlyDetection #Healthcare #MedicalResearch #Wellness #SeniorCare
https://the-14.com/when-everyday-tasks-become-harder-early-clues-to-alzheimers-disease/ -
Knowledge is power when it comes to your child’s health. ❤️
As parents, we worry about everything. But when it comes to pediatric diabetes risk, you don't have to stay in the dark. Symptoms like increased thirst, frequent urination, or unexplained fatigue shouldn't be ignored.
Check your child’s risk level today: https://pedivitals.com/pediatric-diabetes-risk-assessment/
#HealthyKids #DiabetesAwareness #PediatricCare #PediVitals #EarlyDetection #JuvenileDiabetes #WellnessJourney
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@samlitzinger “Early detection is a luxury, that is, until the last 48 hours costs $500K.”
#EarlyDetection -
My younger sister died from a rare type of breast cancer -- and she was in her 40s. Despite my concerns about her symptoms, she decided to wait until her next mammogram -- and by then, the cancer had taken a deep foothold. It can happen to younger women -- unfortunately.
via @Poli_Tics
"Breast cancer increasing in younger women. 'Women under the age of 40, particularly Black women, tend to be diagnosed with more biologically aggressive and rare types of breast cancer, like triple-negative breast cancer and HER2-Positive, according to two other recent studies.' "
#cancer #breastcancer #earlydetection #mammograms #women #healthcare
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Advancing #EarlyDetection in Type 1 #Diabetes: Can we get one step ahead? [Promoted content]: On Wednesday 19 March key stakeholders from across the #diabetes community gathered in Amsterdam for the 18th International Conference on Advanced Technologies and Treatments for Diabetes (ATTD). On this occasion, Breakthrough #T1D NL, Diabetes+, Diabeter, the Dutch Diabetes Association (DVN), Diabetes Fonds and Sanofi, co-organised the #EarlyDetection Policy Forum to advance… https://www.euractiv.com/section/health-consumers/opinion/advancing-early-detection-in-type-1-diabetes-can-we-get-one-step-ahead/?utm_source=mastodon&utm_medium=dlvr.it
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Advancing #EarlyDetection in Type 1 #Diabetes: Can we get one step ahead? [Promoted content] https://www.euractiv.com/section/health-consumers/opinion/advancing-early-detection-in-type-1-diabetes-can-we-get-one-step-ahead/?utm_source=dlvr.it&utm_medium=mastodon
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Did you know? Chronic kidney disease (CKD) often has no symptoms until it's advanced. Early detection is key! Get tested if you have risk factors like diabetes or high blood pressure. #KidneyMonth #EarlyDetection
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Advancing early care for cerebral palsy: the BornToGetThere project [Promoted content] https://www.euractiv.com/section/health-consumers/opinion/advancing-early-care-for-cerebral-palsy-the-borntogetthereproject/?utm_source=dlvr.it&utm_medium=mastodon #celebralpalsy #earlydetection #earlyintervention #earlysurveillance
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UK launches a groundbreaking AI trial to assist radiologists in analyzing breast cancer screenings, enhancing early detection accuracy & reducing diagnosis wait times. A huge step forward in healthcare innovation! #AI #BreastCancer #HealthTech #EarlyDetection #UKHealthcare #Radiology
