Matrix_Medicine

[Matrix_Medicine @matrix_south]

One of the biggest clinical challenges that doctors are faced with daily is trying to predict the outcomes in patients presenting with strokes or CVAs.

Often driven by anxious relatives wanting to know what to expect.

AI is poised to make this challenge significantly easier.

As this study illustrates Machine learning-based models can more accurately predict outcomes compared to standard scoring systems.

‘Machine learning–based models performed better in predicting poststroke outcomes than regression models using the items of conventional stroke prognostic scores.’

@jmirpub

https://t.co/Mbq8p1gSwh

#AIInnovation #AIinMedicine

[Matrix_Medicine @matrix_south]

I second that!

AI applications are going to change the medical world as we know it.

#AIinMedicine

[Elon Musk @elonmusk]

Try using @Grok for a second opinion on medical matters!

[Matrix_Medicine @matrix_south]

This is what I predict the #MedicineOfTomorrow to look like.

AI powered applications working together with doctors to improve healthcare. Not replacing doctors.

‘Before we integrated laboratory data, GPT-4 showed a 60% diagnostic match with physicians on the first differential and 86% in all five. After we integrated the instrumental and laboratory findings, these percentages grew to 72% and 92%, respectively.’

In low resource settings where access to specialist doctors is limited, applications similar to the one used in the study below can significantly improve patient outcomes.

Picture a junior doctor, having worked the whole day and night, being able to use an application that can screen their differential diagnoses for the complex medical patient they see at 03:00 in the morning.

Not only providing better healthcare for the patient but also peace of mind for the doctor.

@EricTopol @DrHughHarvey @AndrewLBeam @ManeeshJuneja @Berci

#AIinMedicine #AIinHealth

https://t.co/Mz3EUr5x20

[Matrix_Medicine @matrix_south]

Healthcare is low-resource settings remains one of the most critical areas needing improvement to better the world we currently live in.

An ever increase disease burden, combined with population growth and a dwindling doctor-to-patient ratio means that in many countries access to healthcare is worse now than what it was previously.

I strongly believe that AI, combined with the pace at which it is improving, can significantly change this.

Ultimately improving patient outcomes and better lives for all.

The role of this account is to highlight areas that are would benefit most from AI intergration together with studies to show that this technology is no longer only a pipe-dream.

Lastly I believe that the future of medicine is not an AI vs Doctor one, as is often the narrative, but rather an AI with Doctor one. Synergistically improving healthcare for all.

#AIinMedicine #MedicinOfTomorrow #AIinHealth

[Matrix_Medicine @matrix_south]

The minicomputers we call smartphones are a powerful tool yet to be fully utilised.

AI powered applications that can be used as point-of-care diagnostics can dramatically change the healthcare landscape in low resource settings.

Studies like the one below highlight this potential.

‘Voice analysis for the diagnosis and prognosis of Parkinson’s disease using machine learning techniques can be achieved, with very satisfactory performance results’

https://t.co/PxHgcorFJV

@ElsevierConnect

#AIinMedicine #MedicineOfTomorrow

[Matrix_Medicine @matrix_south]

In low resource settings paper based handwritten clinical documentation is still the norm.

This is not only inefficient but also takes up valuable time within the consultation in a setting already stretched to the extremes in terms of doctor-to-patient ratios.

Although this study suggests minimal improvements I suggest that if repeated in low resource settings where the current norm formed the control group the results would be significantly better.

‘However, our findings suggest that the tool did not make clinicians as a group more efficient. Future studies can further investigate the utility of DAX for clinician subgroups and alternative implementations with improved clinical adoption.’

https://t.co/mct8pHakUK

@NEJM_AI @NEJM @JWatch

#AIinMedicine #MedincineOfTomorrow #AIIntergration

[Matrix_Medicine @matrix_south]

Breast cancer is the leading diagnosed cancer and the second most common cause of cancer mortality in sub-Saharan Africa.

Often many being diagnosed too late when curable therapies are no longer an option.

This is directly related to lack of access to appropriate healthcare and screening programs

AI has the ability to change this!

A recent study noted that ‘AI correctly identified 47 or 48 of 49 women (96%–98%) with cancer with either portable US or SOC US images’

With the ever increasing access to smartphones and other mobile devices, even in low resource settings, there is an emerging potential to drastically improve healthcare.

Being able to do this type of screening in a rural clinic could expedite diagnoses and streamline referrals.

#AIinMedicine #AIinHealth

https://t.co/a6kFAbJvt3

[Matrix_Medicine @matrix_south]

AI is the answer!

Especially in settings where humans need to interpret subjective data like moving images.

Plugging these images together with the clinical scenario into an AI powered mobile medical application will soon make dilemmas like this a thing of the past.

#AIinMedicine #AIIntegration

[Matrix_Medicine @matrix_south]

This is clearly a Dupuytren’s contracture, often associated with alcoholism.

Now imagine an AI powered medical focused image search engine that would not only produce the above answers and more, but also point to associated illnesses etc.

Taking a photo of your child’s rash and having the trusted AI engine quickly put all your fears to rest.

This is the medicine of the future!

#MedicineOfTomorrow #AIinHealth

[Matrix_Medicine @matrix_south]

Another clinical setting that would benefit from AI intergration in low resource settings.

In countries like South Africa where the current doctor-to-patient ratio is <0.2/1000 people there is not only a lack of skills but also limited time per patient encounter.

Simple AI tools to improve POCUS interpretations would not only improve patient outcomes but also result in ‘faster’ consultations and more time for other patients.

#AIInnovation #AIIntegration #AIinMedicine

[Matrix_Medicine @matrix_south]

I am almost certain the outcomes would be different if the nurses were replaced with AI chatbots like @grok & @ChatGPTapp

Low resource settings are crying out for AI interventions to help deliver good healthcare and ultimately save lives!

#AIinMedicine #AIIntegration #MedicineOfTomorrow

[Matrix_Medicine @matrix_south]

This is a very common clinical dilemma calling for AI intervention.

Scoring systems like CHADsVASc and HAS-BLED are old fashioned are don’t factor in all the social and economic factors that patients in low resource settings often face.

#MedicineOfTomorrow #AIinHealth #AIInnovation #AIinMedicine

[Matrix_Medicine @matrix_south]

@Berci AI will never completely replace human doctors. The future of medicine will require them to work alongside each other. Improving patient outcomes the ultimate goal.

#MedicineOfTomorrow #AiInHealth

[Matrix_Medicine @matrix_south]

AI, used correctly and in the right hands can help prevent the next global pandemic!

‘Researchers have developed a machine learning tool to identify weather and land-use patterns associated with dengue fever transmission in Manila.’

#AIinMedicine #MedicineOfTomorrow #AIInnovation

https://t.co/xCNp3YsToW

[Matrix_Medicine @matrix_south]

One of many such studies highlighting this and other potential benefits.

‘The average time to interpret the 420 images in the validation set was substantially longer for the radiologists (240 minutes) than for CheXNeXt (1.5 minutes).’

https://t.co/cZ0IFKdqBk

[Matrix_Medicine @matrix_south]

AI that has been proven to be non-inferior to Radiologists in various diagnostic investigations is a potential field disrupter and potential life giver in low resource settings.

@Innov_Medicine @NatureMedicine

#AIinMedicine #LMIC #MedicineOfTomorrow