Global Tech Excellence Awards

Wednesday, September 25, 2024

THE EVOLUTION OF GENERATIVE AI: CAPABILITIES, FUTURE, AND IMPLICATIONS FOR CLINICAL RESEARCH

What is generative AI?

Generative AI, in simple terms, is a type of artificial intelligence that can create things on its own, like writing text, generating images, or even composing music. It’s like having a computer program that can come up with new ideas and create content without human input. Generative AI learns from existing data and uses that knowledge to generate new and creative content, making it useful in various fields, from writing stories to designing artwork.

The evolution of generative AI
The early days

The initial forms of generative AI were very basic. It could barely suggest some random words and generate single sentences based on traditional machine learning algorithms which were not very useful. The main challenge was to make these systems ‘learn’ like a human does. Examples are the phone text and Gmail.

Deep learning and neural networks

The innovation in AI has advanced rapidly with the advent of deep learning and neural networks, which mimic the human brain’s structure and functions. This allowed AI models to ‘learn’ from data much more efficiently.

GPT and beyond

Fast-forward to today, and we have mind-bogglingly advanced models like GPT (Generative Pre-trained Transformer). These models can write articles, hold a conversation, keep contextual understanding, provide multilingual support, and even undertake logical reasoning.
Capabilities

So, what can modern generative AI do?

Content creation: Write articles, stories, or even generate artwork.
Data simulation: Generate realistic datasets for testing.
Personal assistants: Help in automating tasks and answering queries.
Language translation: Translate languages with high accuracy.
Write code: It can write new code, optimize the existing code, debug code.
Create data visualizations: Helps in creating the visualizations for the given dataset.
The future ahead

As promising as Generative AI is, it remains in its infancy, and we’ve barely scratched the surface. It’s very similar to exploring the capabilities of the human brain; we have yet to discover its full potential. Unlocking meaningful and consistent responses from Generative AI requires asking the right questions or providing the proper prompts. Organizations worldwide are recognizing the importance of Generative AI and the pivotal role of prompt engineers. These specialized individuals possess the necessary skills to interact with AI effectively, ensuring relevant and accurate responses.

With this exciting landscape in mind, let’s contemplate what the future holds:Ethical considerations: As AI gets better at generating content, questions about misinformation and data privacy will become more crucial.

Collaboration with humans: AI will work alongside humans to create even more sophisticated content.
Adaptability: Future AI will adapt to individual user needs more efficiently.
Role of generative AI in clinical Research

Generative AI holds the potential to revolutionize the healthcare industry, especially in the realms of clinical research and trials. Some areas where it can make a significant impact include:

Conclusion

Generative AI has come a long way from its humble beginnings. With its ever-expanding capabilities, it promises a future where machines can aid human creativity and problem-solving in unprecedented ways. The potential applications in clinical research and trials are particularly exciting, promising faster, more efficient, and more ethical healthcare solutions.

Website: International Research Awards on Computer Vision #computervision #deeplearning #machinelearning #artificialintelligence #neuralnetworks, #imageprocessing #objectdetection #imagerecognition #faceRecognition #augmentedreality #robotics #techtrends #3Dvision #professor #doctor #institute #sciencefather #researchawards #machinevision #visiontechnology #smartvision #patternrecognition #imageanalysis #semanticsegmentation #visualcomputing #datascience #techinnovation #university #lecture #biomedical

Visit Our Website : computer.scifat.com Nomination Link : computer-vision-conferences.scifat.com/award-nomination Registration Link : computer-vision-conferences.scifat.com/award-registration Member Link : computer-vision-conferences.scifat.com/conference-abstract-submission Awards-Winners : computer-vision-conferences.scifat.com/awards-winners Contact us : computer@scifat.com

Get Connected Here:

==================

Social Media Link

Twitter : x.com/sarkar23498 Pinterest : in.pinterest.com/computerconference22 Blog : computervisionawards.blogspot.com Instagram : www.instagram.com/saisha.leo Tumbler : www.tumblr.com/blog/shulagnasarkar22 YouTube : www.youtube.com/channel/UCUytaCzHX00QdGbrFvHv8zA

Tuesday, September 24, 2024

How to Use Machine Learning for Weather Predictions

Machine learning (ML) is the latest buzz. In fact, it has been a buzz for a year or two from now. Everyone and everywhere are talking about its capabilities. Now, it is at the forefront when ChatGPT and other generative AI tools are helping the mass in drafting emails and solving homework. It is also at the forefront to sophisticated image models that generate photos and edit photos as well. In fact, it is at the forefront of technological innovation. Most of the attention today has been on the consumer-focused applications and amid such a scenario a quieter revolution has been unfolding in a more unexpected area. It is weather forecasting.

Rise of ML in Weather Forecasting

Weather forecasting relied heavily on numerical models in traditional methods. The models used mathematical equations to predict future weather conditions. ML today has emerged differently and it is promising of course. The ML tools are enhancing weather forecasts. ML techniques have been explored for Earth system modeling at the European Centre for Medium-Range Weather Forecasts (ECMWF). It has been particularly used in neural networks to integrate satellite observations better.

ECMWF has been experimenting with ML-based weather forecasting since 2018. The initial attempts were modest and basically used simple models and low-resolution data. The early models witnessed some success, but were nowhere near as accurate as the sophisticated numerical models that ECMWF already used. Hence, the consensus was that ML could be an interesting research tool. It was unlikely to replace traditional methods in near future.

Rapid Evolution

The landscape changed rapidly between February 2022 and April 2023. Several papers from large tech giants including NVIDIA, Huawei and Google DeepMind demonstrated some improvements. Gradually, the new models started to rival the skill of ECMWF’s traditional models.

Efficiency is catchy and remarkable about the ML models. The models require just a single GPU and thereafter can produce a forecast in just about minute of time. It also uses just a fraction of the energy that traditional models consume.

How ML Models Work

The current generation of ML models for weather forecasting relies heavily on traditional numerical models for training and validation data. ECMWF’s Integrated Forecasting System (IFS) is a good example here. It is used to create the datasets that these ML models learn from.

ML models are starting to show impressive results despite reliance on traditional data. For instance, Huawei’s Pangu-Weather and NVIDIA’s FourCastNet have been able to match the performance of ECMWF’s IFS in several cases. The ML models have proven to be competitive with traditional forecasting methods when evaluated with metrics like root mean square error (RMSE) and anomaly correlation coefficient (ACC).

Challenges of ML-Based Forecasting

The ML-based weather forecasting is not free from challenges. The challenges need to be addressed and one main issue is that the ML models are trained to minimize errors across all types of weather conditions. This can sometimes lead to overly smoothed predictions. Hence, the ML models can be generally accurate, but they may not always capture extreme weather events with the same intensity as traditional models. For example, in the case of tropical cyclone Freddy in 2023, Pangu-Weather accurately predicted the path of the storm, but simultaneously failed to fully capture the intensity of the cyclone-related winds.

The limitation is not a flaw in the ML models. It is a result of the way the models are trained. Researchers are experimenting with new training methods to encourage the models to make more extreme predictions.

Future of Weather Forecasting

The potential benefits of ML-based weather forecasting are enormous despite the challenges. The models are highly efficient and could also enable the creation of high-resolution weather ensembles with hundreds of members.

Moreover, the ML models can easily be integrated with traditional forecasting methods. The models can be used in providing rapid initial forecasts and hence may also be refined by using more detailed numerical models. The hybrid approach may even help in validating and improving both types of models.

Embracing the Future

ML is evolving continuously and it is becoming clear that it has the potential to revolutionize weather forecasting. However, it does not mean that the traditional models are becoming obsolete. Meteorological centers have access to vast amounts of data and this makes them ideally perfect to lead the development of new ML-based forecasting techniques.

Get Connected Here:

==================

Social Media Link

Monday, September 23, 2024

Machine learning early warning system reduces non-palliative deaths in general medicine unit

Background

Estimating, preventing, and reacting to the clinical deterioration of hospitalized individuals is critical to increasing patient safety. Unidentified clinical deterioration is the primary cause of unnecessary admissions to the intensive care unit (ICU), resulting in prolonged stays and increased fatality. Despite the extensive usage of prediction tools, the evidence for their usefulness is inconsistent.

A Kaiser Permanente study of 19 hospitals in Northern California revealed that an automated risk estimation model with remote nurse monitoring and on-the-ground actions by quick response teams reduced 30-day mortality by 16%. However, the technological and clinical characteristics of advanced early alert systems that might enhance clinical outcomes are unknown.

About the study

The present study investigated whether CHARTwatch could improve patient deterioration-related clinical outcomes.

The program predicts patient deterioration by using real-time data from electronic medical records. The time-aware multivariate adaptive regression spline (MARS) technique considered risk score projections from past encounters, changes in risk ratings since previous assessments, and time-series summaries.

The model communicated to nurses and physicians via texts and email, and it included a clinical route for the high-risk patient category, such as physician evaluation within an hour, increased vital sign monitoring, and alerts for palliative care consultations.

Patients admitted to St. Michael’s Hospital’s general internal medicine (GIM) unit received the intervention between 1 November 2020 and 1 June 2022. The pre-interventional period was between 1 November 2016 and 1 June 2020.

Propensity score-based weighting compared intervention recipients to individuals admitted before the intervention. Difference-indifferences assessment compared intervention recipients in the general internal medicine unit and non-recipients in the respiratory, nephrology, and cardiology units.

The primary endpoint was within-hospital mortality from non-palliative care, defined as fatalities that did not result from a recorded palliative care treatment.

Secondary endpoints were palliative deaths, total deaths, and transfers (a composite measure of deaths among palliative care recipients or shifts to inpatient palliative care units), ICU transfer, a composite measure of transfer to ICUs or mortality, and hospital stay length.

The International Classification of Diseases, tenth revision, Canadian version (ICD-10-CA), ascertained patient diagnosis. Researchers retrospectively calculated model predictions for control group patients.

Clinicians received alerts only in the interventional period for GIM unit patients. The study excluded individuals with coronavirus disease 2019 (COVID-19) or influenza and those with preadmission palliative care comorbidities. Logistic regressions estimated propensity scores for the GIM and subspecialty cohorts.

Researchers calculated the relative risk (RR) for analysis, adjusting for study covariates. Poisson regressions compared binary outcomes, and linear models compared continuous outcomes.

Study covariates included age, gender, comorbidities, hospitalizations in the prior six months, hospitalization month, vital signs, homelessness, neighborhood racial and new populations, neighborhood material resources, and admission to the ICU before transfer to subspecialty wards or GIM units.

Results

The analysis comprised 13,649 GIM unit admissions and 8,470 subspecialties unit admissions. In the general internal medicine unit, 482 patients became high risk in the interventional period, and 1,656 patients became high risk during the control period.

Non-palliative mortality was significantly lower during the interventional period than before the intervention among GIM patients (1.60% vs. 2.10%; RR, 0.7) but not among subspecialty unit patients (1.90% vs. 2.10%; RR, 0.9).

Among GIM patients at high risk of deterioration for whom CHARTwatch provided one or more alerts, the non-palliative mortality rates were 7.1% during the interventional period and 10% before the intervention (RR, 0.7).

The team found no significant difference in the subspecialty groups (10% vs. 11%; RR of 0.98). Difference-indifference assessments yielded an RR reduction of 0.8 for mortality from non-palliative care in the general internal medicine unit.

In the held-out testing data, the model demonstrated 53% sensitivity and 31% positive predictive value (PPV) in detecting clinical deterioration during hospitalization (death or transfer to the ICU, step-up care, or palliative care unit).

Compared to the pre-interventional period, the intervention resulted in considerably more antibiotic and corticosteroid prescriptions and increased vital sign monitoring. These data indicate that the intervention was related to enhanced patient monitoring and therapies that might slow deterioration.

Conclusion

The study showed that deploying CHARTwatch for GIM admissions was related to a decreased probability of mortality from non-palliative care compared to the preintervention period.

The results show that early alert systems based on machine learning are potential technologies to improve healthcare outcomes.

However, one should interpret findings cautiously due to the potential unmeasured confounding. Future studies will assess equity-related factors of the intervention and the qualitative perspectives of clinical team members.

Get Connected Here:

==================

Social Media Link

Saturday, September 21, 2024

Veterinary medicine makes use of artificial intelligence

Artificial intelligence and machine learning are becoming part of everyday life very rapidly. From smart watches that track your sleep to apps on your smartphone that use artificial intelligence to provide information in seconds, AI is becoming more and more commonplace.

My simplified understanding of AI is that this field of computer science is developing methods and software to enable machines to perceive their environment and use learning and intelligence to take certain actions.

Will AI become part of veterinary medicine? Will veterinarians be replaced by artificial intelligence tools, or will they use them as part of their practice?

As might be expected, the future is already here in some respects.

A previous American Animal Hospital study surveyed veterinarians, and 83 per cent of respondents were familiar with AI and nearly 30 per cent of those veterinarians were already incorporating it into their practice.

A simple example of an AI tool in veterinary medicine might be a “voice to text” tool that can transcribe a conversation between a veterinarian and a client and produce a written document that can become part of the medical record.

Medical recordkeeping is an important task, but it is usually not something that veterinarians enjoy about their job. Having a tool like this frees up the veterinarian’s time to take on more rewarding tasks.

Other examples that are already being used in agriculture include many of the software programs found in dairy barns that use robotic milkers and other advanced technologies such as pedometers, feed intake or standing/lying time. These tools can predict estrus cycles or when a cow might require treatment due to illness.

A 2020 study published in the Royal Society of Open Science investigated an ear sensor in sheep that contained an accelerometer and gyroscope.

A wide variety of these kinds of products are commercially available now that can track activities such as walking, standing and lying in a variety of livestock species. The researchers in this study were able to use this data to predict lameness in sheep with an accuracy of almost 85 per cent.

In another study, published in November 2023, researchers used an AI tool to predict body condition score in dairy cows, beef cattle and pigs by using quantitative analysis of three-dimensional shapes.

Imagine having a camera in the pasture that automatically records photos of cattle and provides an accurate estimate of their body condition on a regular basis without the producer needing to evaluate them at all.

I recently read a research paper in the November 2024 issue of Computers and Electronics in Agriculture in which researchers developed an AI tool that used retinal images in cattle to accurately diagnose cardiovascular disease.

Is cardiovascular disease the most important disease issue in beef cattle? Probably not — we see it occasionally in feedlot cattle and sometimes because of hardware disease or severe lung damage.

However, this is probably just the tip of the iceberg. New AI tools will be developed to help producers and veterinarians with the day-to-day tasks of finding and selecting sick animals, diagnosing disease and monitoring productivity.

Artificial intelligence tools are already here, and there is little doubt that the progress in this area will continue to accelerate. These tools will help to optimize the diagnostic process and enhance animal welfare.

We are probably still a long way from the day where we will be replacing animal health attendants and veterinarians with AI tools, but there will certainly be many new opportunities to explore in this exciting field.

Get Connected Here:

==================

Social Media Link

Thursday, September 19, 2024

A First-of-its-Kind Machine Learning Model to Flag, Screen People for Elevated Lipoprotein(a)

The Family Heart Foundation has announced the successful completion of the Flag, Identify, Network and Deliver™ “FIND Lp(a)” , a machine learning model which can identify people who are likely to have elevated Lp(a).

Current understanding pertaining to the prevalence and awareness of the role of Lp(a) in cardiovascular disease is lacking. The FIND Lp(a) machine learning model is designed to support at-risk individuals by engaging all health care stakeholders and provides optimal support to flagged individuals, by developing and promoting best practices to support adoption of broad screening of the condition.

This machine learning model was developed using the Family Heart DatabaseTM of medical claims , and has demonstrated 60% precision. This model, as researchers noted, provides decision-support to clinicians by identifying a target group for this initial screening initiative. The data enables health care providers to focus their efforts and maximize the use of limited resources. Moreover, the model benefits health systems by connecting their patients with education and individualized support.

“As a preventive cardiologist, I know how critical it is that we identify individuals with high Lp(a) early. It is equally important that patients with high Lp(a) are aware of the risk factors for cardiovascular disease and receive aggressive treatment for these risk factors, including controlling blood pressure, diabetes and cholesterol,” said Ijeoma Isiadinso MD, MPH, Emory Center for Health Disease Prevention via a press release. “The FIND Lp(a) partnership with the Family Heart Foundation will dramatically increase the number of individuals being screened for high Lp(a) and empower our patients with quality education and resources to manage their diagnosis. Time is of the essence. We are working together to identify and help individuals with high Lp(a) now.”

Get Connected Here:

==================

Social Media Link

Wednesday, September 18, 2024

The Evolution of job markets in an AI-driven world

One of the most profound impacts of AI on the global economy is its effect on the labor market. The rise of AI-driven automation is creating both opportunities and challenges for workers across different industries. On one hand, AI is replacing repetitive and mundane tasks, freeing up human workers to focus on higher-value activities. On the other hand, the displacement of jobs by automation is raising concerns about the future of employment for millions of people worldwide.

In industries such as manufacturing, logistics, and customer service, AI-powered machines are increasingly taking over tasks that were previously performed by humans. Robots and autonomous systems can now handle everything from assembling products to managing inventories and interacting with customers. While this shift leads to significant gains in efficiency and productivity, it also threatens the livelihoods of workers whose jobs are being automated.

However, AI is also creating new opportunities in the job market, particularly in fields that require specialized skills in AI development, data science, and robotics engineering. As companies integrate AI into their operations, the demand for workers who can design, implement, and manage AI systems is growing rapidly. These new roles often require a higher level of education and training, underscoring the importance of upskilling and reskilling the workforce to meet the demands of the AI-driven economy.

Moreover, the restructuring of job markets due to AI is pushing companies and governments to rethink education and training systems. Lifelong learning is becoming essential, as workers need to continuously update their skills to stay competitive in the rapidly evolving job market. Governments, in particular, are playing a crucial role in developing policies that support workforce transitions, providing resources for retraining programs, and ensuring that workers have access to the tools they need to succeed in the AI era.

Get Connected Here:

==================

Social Media Link

Monday, September 16, 2024

Maritime artificial intelligence market nearly triples in size over past year

New research by British consultancy Thetius estimates that the maritime artificial intelligence (AI) market has nearly tripled in size in the space of just one year and is now valued at $4.13bn, with a projected five-year compound annual growth rate of 23%.

The 29-page report, commissioned by Lloyd’s Register, highlights six critical areas of AI application, including data-driven condition-based maintenance and port management.

When deploying AI technologies, the report argues that it makes sense to do so in iterations to ensure best risk management. For example, start by using AI and machine learning to automate repetitive easy processes, giving people the opportunity to focus on more complex tasks. An easy example, according to Thetius, is email organisation.

“Streamline time-consuming processes, then move on to bigger tasks before tackling the seriously complex ones,” the report advises, adding: “Implementing AI solutions incrementally allows for testing and optimisation at each stage. This approach helps to identify potential issues early and make necessary adjustments without disrupting operations.”

90% of global workforces spend 60% of their time on recurring tasks

Commenting on the new study, Mark Warner, global content and communications director at Lloyd’s Register, said: “The findings of the report show that the maritime sector, often perceived as traditional and resistant to change, is now embracing AI with remarkable enthusiasm. This shift is driven by the need for greater operational efficiency, enhanced safety, and a commitment to sustainability. AI technologies are being harnessed to optimise voyages, predict maintenance needs, enhance navigational safety, and manage energy consumption more effectively.”

Writing for Splash last week, Frank Coles, the former head of Wallem Group, pointed out that AI remains rather opaque for many in shipping.

“A lot of what is called AI in the maritime marketing blurb is more machine learning than the generative AI using neural networks in deep learning,” Coles wrote, adding that generative AI could disrupt shipping in ways nobody has even considered at the moment.

Commentators have been waxing lyrical about how AI is set to transform the maritime industry, and with good reason. Increasingly compelling use cases are emerging in applications such as route optimisation, safety of navigation, fuel efficiency, compliance, predictive maintenance, cargo management and many more.

However, amidst the hype there is a need for concerted industry effort in developing collaborative forum and policy frameworks.

“We are seeing early adopters within maritime sector embrace AI with varying degrees of effectiveness. There however seems to be a trend to stick GPT at the end of maritime terms and rush out applications as businesses hurriedly take their seats in the maritime AI theatre,” commented Manish Singh, CEO of Aboutships, in conversation with Splash earlier this year. “Often this is happening pre-maturely, without optimal data architecture and before appropriate policy and resilience frameworks are put in place.”

In Splash’s 2024 maritime tech forecaster, Lasse Kristoffersen, president and CEO of Wallenius Wilhelmsen, predicted: “Generative AI will in shipping start to deliver real impact and many companies will start utilising technology like Copilot from Microsoft.”

Hafnia, the world’s largest product tanker owner, is behind one of shipping’s most keenly watched foundational AI joint ventures, Complexio.

Foundational AI’s main focus is to enable human-machine collaboration by connecting to all areas of a company’s infrastructure and providing a centralised hub that serves as the primary point of AI-driven analysis and decision-making. This transformational approach takes the role of AI from isolated applications in separate departments to build a unified, integrated system that enhances the entire organisation’s performance and productivity.

“90% of global workforces spend 60% of their time on recurring tasks. In an AI-first world, we can greatly reduce this by automating the processing of routine tasks, like assembling a ship’s clearance package and simultaneously provide our leadership teams with enhanced macro views of business operations, enabling them to identify areas to improve upon,” Hafnia CEO Mikael Skov commented earlier this year

Get Connected Here:

==================

Social Media Link

Friday, September 13, 2024

Study uncovers biases in AI’s medical image analysis

AI models help diagnose medical conditions from images like X-rays. However, studies reveal they can perform unevenly across demographics, often less accurately for women and people of color. Surprisingly, MIT researchers found in 2022 that AI can predict a patient’s race from chest X-rays, a skill beyond even expert radiologists

The research team discovered that the most accurate model predicting demographics also showed significant “fairness gaps.” These gaps mean the models have varying accuracy in diagnosing images of people from different racial or gender groups, often leading to incorrect results for women, Black individuals, and other groups.

According to Marzyeh Ghassemi, an MIT professor involved in the study, machine-learning models excel at predicting demographics like race or gender. However, this ability correlates with their uneven performance across diverse groups, a connection that has yet to be established.

The researchers also found they could improve fairness by retraining the models to reduce biases. However, this “debiasing” method worked best when the models were tested on patients similar to those they were initially trained on, such as from the same hospital. When applied to patients from different hospitals, the fairness issues resurfaced.

Haoran Zhang, an MIT graduate student and lead author of the paper, emphasizes two main points: first, external models should be rigorously evaluated on your data because fairness assurances from developers may not apply to your specific population.

Second, train models using your own data whenever possible to ensure relevance and accuracy. Yuzhe Yang, another MIT graduate student, and lead author, collaborated on the study published in Nature Medicine. Co-authors include Judy Gichoya from Emory University School of Medicine and Dina Katabi from MIT.

As of May 2024, the FDA has approved 882 AI-enabled medical devices, 671 of which are tailored for radiology. Since 2022, when researchers demonstrated AI’s capability to accurately predict race from diagnostic images, further studies have revealed that these models also excel at predicting gender and age despite not being explicitly trained for these tasks.

Marzyeh Ghassemi, an MIT professor involved in the research, notes that many machine learning models can predict demographics, surpassing human capabilities in some aspects. However, during training, these models inadvertently learn to predict non-medical factors, which may not align with clinical goals.

In their study, researchers aimed to investigate why AI models perform differently for various groups. They examined if these models used demographic cues, which could lead to less accurate predictions for specific groups. This happens when AI relies on demographic factors rather than other image features to diagnose medical conditions.

Using chest X-ray data from Beth Israel Deaconess Medical Center, the researchers trained models to detect fluid buildup in lungs, collapsed lungs, or enlarged hearts. They then tested these models on new X-rays not used during training. While the models generally performed well, they showed “fairness gaps” — differences in accuracy rates between men and women and between white and Black patients.

The models accurately predicted the gender, race, and age of the X-ray subjects. Interestingly, there was a strong link between how well the models predicted demographics and the size of their fairness gaps. This suggests that the models use demographic information as a shortcut when making disease predictions.

To address these fairness gaps, the researchers tested two approaches. First, they trained some models to prioritize “subgroup robustness,” rewarding them for better performance on the subgroup with the worst accuracy and penalizing higher error rates for one group compared to others. Second, they employed “group adversarial” techniques to remove demographic cues from the images entirely. Both methods proved effective in reducing fairness gaps.

According to Marzyeh Ghassemi, these methods can mitigate fairness issues without sacrificing overall performance when dealing with data from similar distributions. Subgroup robustness encourages models to be sensitive to mispredictions in specific groups. In contrast, group adversarial methods aim to eliminate group information.

The approaches to reduce bias in AI models only worked well when tested on data similar to what they were trained on, such as the Beth Israel Deaconess Medical Center dataset. When these “debiased” models were tested on data from five other hospitals, their overall accuracy remained high but showed significant fairness gaps.

Haoran Zhang highlighted that debiasing a model on one dataset doesn’t guarantee fairness when applied to patients from different hospitals. This is concerning because many hospitals use models trained on data from other institutions, which may need to generalize better.

Marzyeh Ghassemi emphasized that even state-of-the-art models optimized for specific datasets may only perform optimally across some patient groups in new settings. She noted that models often trained on one hospital’s data are then broadly deployed, potentially leading to inaccurate results for specific groups.

The researchers observed that models debiased using group adversarial methods showed slightly better fairness on new patient groups than those using subgroup robustness. They plan to explore and test additional methods to develop models to make fair predictions across diverse datasets.

The study suggests hospitals should thoroughly evaluate AI models on their patient populations before widespread deployment to ensure accurate and fair outcomes for all groups.

Get Connected Here:

==================

Social Media Link