PCASO Laboratory

Penn Computer Assisted Surgery and Outcomes Laboratory @PennSurgery @GRASPlab | PI: @laparoscopes | Tweets/RTs do not reflect university or dept views

[Jayson Marwaha, MD MSc @Jayson_Marwaha]

In this era of AI excitement, how do we ensure surgeons' expectations are appropriately aligned with the actual capabilities of new surgical AI tools? In @JAMASurgery, @Laparoscopes & I comment on some important dimensions of user calibration to new surgical AI systems.

[PennSurgery @pennsurgery]

Collaborative project b/w @pcasolab @laparoscopes and @kostasPenn has been awarded 1 of 4 Discovering the Future of AI awards and will use #AI to reconstruct four-dimensional surgical environments from video to assess skill, predict risk, and improve patient outcomes.
More information at:
https://t.co/lTMfNzX3zT

[PCASO Laboratory @PCASOLab]

Congrats @shubha_vasisht on an incredibly productive 2 years @pennsurgery @PCASOLab!

Good luck on your next chapter @CWRUSOM! You're going to be an amazing physician-scientist!

[Bowen Wang @NU_JEANSS]

Very cool tool to assess our #AI literacy @PCASOLab

[Dan Hashimoto, MD MS FACS @Laparoscopes]

So very excited to be @NMSurgery for the privilege of being the Edelstone-Bendix Visiting Prof during the Dr Pearce Symposium

More on Dr Bendix & Mr Edelstone at https://t.co/KXS9ctxpyb

@pennsurgery @PCASOLab

[Dan Hashimoto, MD MS FACS @Laparoscopes]

Great job by @pennsurgery @PCASOLab Harrison scholar in representing Phila Academy of Surgery at the Tripartite Surgical Meeting w/ her research on #AI for POEM.

Great questions by @BostonSurgical discussant @bratogram @BIDMCSurgery

[PCASO Laboratory @PCASOLab]

@AimeeGthePHD @Surg_Education @SurgEdFellows @GSE_JASE Congrats! @surg_education is in such great hands under your leadership!

[Dan Hashimoto, MD MS FACS @Laparoscopes]

It was such an incredible privilege to have been asked to give a keynote at the 125th Japan Surgical Society & to cover how #computervision #robotics #endoscopy will change surgery

Thanks @pennsurgery @PCASOLab @GRASPlab for support of multidisciplinary CS+surgery research

[Sharon L. Stein @slsteinmd1]

This week, I am joined by Dr. Daniel Hashimoto @Laparoscopes - who is changing the way we think about artificial intelligence in #surgery.

Listen to the whole episode to learn more about #AI in surgery:

🔗https://t.co/UswwvSNXF9

#AI #surgeons #podcast #podcast

[PCASO Laboratory @PCASOLab]

Come by poster 164 at 11:15 to learn more from @Qiu84046087 re weakly supervised learning for video & how we tackle transient object presence in surgical video & beyond! #WACV2025

Paper: https://t.co/vumVBXI2GW

Video: https://t.co/kMd4JS1n49

@pennsurgery @GRASPlab @CIS_Penn

[PennSurgery @pennsurgery]

New in @JAMASurgery @pcasolab's @Laparoscopes et al offer a practical guide on the use of simulation and video data in surgical research.
Part of @JAMASurgery’s Guide to Statistics and Methods series on Big Data Research in Surgery. https://t.co/YHXgycMQYB

[PCASO Laboratory @PCASOLab]

Happy holidays from the PCASO Lab team!

Thanks to everyone who made it to breakfast and missing those already out of town!

[Dan Hashimoto, MD MS FACS @Laparoscopes]

Great job by @pennsurgery @PCASOLab Harrison scholar @shubha_vasisht on her @AmCollSurgeons #ACSCC24 oral presentation on PPI use in patients who underwent POEM vs Heller

Great job presenting as a premed student!!!

Fyi med schools, she's applying this year!

[PCASO Laboratory @PCASOLab]

Not every day the lab gets to publish with 2 Nobel laureates!

Congrats @DAcemogluMIT & Simon Johnson on the 2024 Nobel in economics!

@pennsurgery @Laparoscopes @viveksinghmed @PennMedCSO

[Valerio Capraro @ValerioCapraro]

Many people believe that AI advances will dramatically increase inequality.

In a paper with two Nobel laureates, Daron Acemoglu and Simon Johnson, plus 30 multidisciplinary experts, we argue that it’s more complex than a simple “rich-get-richer” story.

For example, we coined the term “inverse skill bias” to describe an emerging pattern: generative AI seems to benefit low-skilled workers more than high-skilled ones.

We also suggest generative AI may reduce racial and gender bias in healthcare and education.

However, some inequalities could indeed worsen.

For example, companies with access to more data may gain an anticompetitive advantage, exerting market power over smaller firms. Additionally, companies may be incentivized to automate work rather than invest in enhancing and complementing human capabilities.

Gender bias in career achievement may also worsen, as preliminary evidence shows that men are using chatbots more than women, leading to an increase in productivity among men but not women.

We argue institutions will play a critical role in sharing AI’s benefits equitably. Unfortunately, current regulations fall short of addressing inequalities and fostering shared prosperity.

Our paper ends with six policy suggestions we believe can help reduce socioeconomic inequality:

1) Create a more balanced tax structure, equating marginal taxes on hiring, training, and AI investments.

2) Engage workers and civil society in AI shifts, and establish data unions for control over data.

3) Boost support for research into human-complementary AI tools to enhance productivity and skillsets.

4) Train professionals, especially in healthcare and education, in AI use, including ethical aspects.

5) Invest in tools to counter AI-generated misinformation and in education on misinformation.

6) Embed AI expertise in government for sector-wide decision support.

Read the full paper here: https://t.co/h9YzpZLoDX

Thank you to an amazing list of coauthors, without whom this work wouldn’t have been possible:

@AustinLentsch @DAcemogluMIT @SelinAkgun9 Aisel Akhmedova @EBilancini @JFBonnefon @BehSnaps @lu_butera @Karen_Douglas @JimACEverett Gerd Gigerenzer @chrisgreenhow @Laparoscopes @PCASOLab @jholtlunstad @jetten_j @baselinescene @werkunz @longoni_chiara Pete Lunn @simone_natale Stefanie Paluch @iyadrahwan Neil Selwyn @viveksinghmed @ssuri Jennifer Sutcliffe @JoePTomlinson @Sander_vdLinden @PaulvanLange @FriederikeWall @jayvanbavel Riccardo Viale

[Valerio Capraro @ValerioCapraro]

Many people believe that AI advances will dramatically increase inequality.

In a paper with two Nobel laureates, Daron Acemoglu and Simon Johnson, plus 30 multidisciplinary experts, we argue that it’s more complex than a simple “rich-get-richer” story.

For example, we coined the term “inverse skill bias” to describe an emerging pattern: generative AI seems to benefit low-skilled workers more than high-skilled ones.

We also suggest generative AI may reduce racial and gender bias in healthcare and education.

However, some inequalities could indeed worsen.

For example, companies with access to more data may gain an anticompetitive advantage, exerting market power over smaller firms. Additionally, companies may be incentivized to automate work rather than invest in enhancing and complementing human capabilities.

Gender bias in career achievement may also worsen, as preliminary evidence shows that men are using chatbots more than women, leading to an increase in productivity among men but not women.

We argue institutions will play a critical role in sharing AI’s benefits equitably. Unfortunately, current regulations fall short of addressing inequalities and fostering shared prosperity.

Our paper ends with six policy suggestions we believe can help reduce socioeconomic inequality:

1) Create a more balanced tax structure, equating marginal taxes on hiring, training, and AI investments.

2) Engage workers and civil society in AI shifts, and establish data unions for control over data.

3) Boost support for research into human-complementary AI tools to enhance productivity and skillsets.

4) Train professionals, especially in healthcare and education, in AI use, including ethical aspects.

5) Invest in tools to counter AI-generated misinformation and in education on misinformation.

6) Embed AI expertise in government for sector-wide decision support.

Read the full paper here: https://t.co/h9YzpZLoDX

Thank you to an amazing list of coauthors, without whom this work wouldn’t have been possible:

@AustinLentsch @DAcemogluMIT @SelinAkgun9 Aisel Akhmedova @EBilancini @JFBonnefon @BehSnaps @lu_butera @Karen_Douglas @JimACEverett Gerd Gigerenzer @chrisgreenhow @Laparoscopes @PCASOLab @jholtlunstad @jetten_j @baselinescene @werkunz @longoni_chiara Pete Lunn @simone_natale Stefanie Paluch @iyadrahwan Neil Selwyn @viveksinghmed @ssuri Jennifer Sutcliffe @JoePTomlinson @Sander_vdLinden @PaulvanLange @FriederikeWall @jayvanbavel Riccardo Viale

[Dan Hashimoto, MD MS FACS @Laparoscopes]

Thrilled to organize the new @MICCAI_Society #clinicaltranslation series ft @PennRadiology's @asset25 as our 1st speaker & @cekahn as moderator

Learn more about clinical problems for #medicalimaging
#AI
@PennSurgery @PennEngineers @CIS_Penn @GRASPlab @PennHealthTech

[Dan Hashimoto, MD MS FACS @Laparoscopes]

Great to have @ChildrensNatl @ddonoho giving grand rounds for @PennNSG on #surgicaldatascience!

[Dan Hashimoto, MD MS FACS @Laparoscopes]

Really excited that the @AmCollSurgeons #HealthIT Committee's session on #generativeAI for #surgery added to the #ACSCC2024 agenda!

We'll be diving into practical applications + safety considerations of #genAI for surgeons

#surgtwitter

[PCASO Laboratory @PCASOLab]

Article available as open access at https://t.co/8fXuBc8iyb

[PCASO Laboratory @PCASOLab]

New from @matjogan & the @pennsurgery @PCASOLab team in @Surg_Education's Global Surgical Education journal

What role does #dataquality & #uncertainty play in #AI models for surgery? How does it impact #bias?