List out the challenges of ML/ AI for delivering clinical impact – Pubrica

1. LIST OUT THE CHALLENGESOF MACHINE LEARNING/ ARTIFICIAL INTELLIGENCEFOR DELIVERING CLINICALIMPACT An Academic presentationby Dr.NancyAgnes,Head,TechnicalOperations,Pubrica Group: www.pubrica.com Email:sales@pubrica.com

2. Today'sDiscussion Outline In-Brief Introduction Challenges of Machine learning in Clinical Sectors Conclusion

3. In-Brief The exciting promise of artificial intelligence in healthcare has been widely reported, with potential applications across many different domains ofmedicine. This promise has been welcomed as healthcare systems globally struggle to deliver the experience of healthcare, improving the health of populations, decreasing capita costs of healthcare and improving the work-lifeofhealthcare providers. Pubrica explores the main challenges and limitations of AI in healthcare and considers the steps required to translate these potentially transformative technologies from research to clinicalpractice.

4. Introduction A rapidly increasing number of academicresearch studies have demonstrated the various applicationsofAI in healthcare, including algorithms for interpreting chest radiographs detecting cancer in mammograms,etc. Applications have also been shown inpathology identifying cancerous skin lesions diagnosing retinal imaging detecting arrhythmias and even identifying certain diseases fromelectrocardiograms. Contd..

5. Analysis of the volume of data collected from electronic health records offers promise in extracting clinical information and making the diagnosis andproviding real-time risk scores for transferring care predicting in-hospital mortality, prolonged length of stay, readmission risk and discharge diagnoses predicting futuredeterioration. Proof concept studies aimed to improve the clinical workflow, including automatic extraction of semantic information from transcripts, recognizing speech indoctor- patient conversations, predicting the risk of failure to attend hospital appointments, and even summarising doctor-patientconsultations. Contd..

6. The impressive array of studies, it is perhaps surprising thatreal-world deployments ofmachine learningin clinical practicearerare. AI possess a positive impact on many aspects of medicine and can reduce unwarranted variation in clinical practice, improve efficiency and prevent avoidable medical errors that will affect almost every patient during their lifetimein a systematic Review Writing.

7. Challenges ofMachine learning in Clinical Sectors Particularly critical for algorithms in EHR, it is easyto ignore that all input data are generated within anon- stationary surrounding with shifting patients, whereclinical and operational practices develop using asystematic Review writing Services. The arrival of a new predictive algorithm may produce alterations in routine, resulting in distribution comparedto train thealgorithm. 1.Dataset Shift Methods to analyze drift and update models inresponse to deteriorating performance areessential. Contd..

8. Mitigations to manage this effect include the likely requirement for periodical retraining along with the careful quantification of performance over timeto identify problems with systematic reviewservices. Data-driven testing procedures recommend the most appropriate updation method, from easy recalibration to full model retraining, to stabilize performance over time after conductinga systematicreview. Contd..

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10. 2. Achieving Robust Regulation andRigorous QualityControl A fundamental component of achieving safeand effective deployment of artificial intelligence algorithms is the development of thenecessary regulatoryworks. It holds a unique challenge given the current pace of innovation, significant risks involved, and the potentially fluid nature of machinelearning models says a systematic reviewpaper. Proactive regulation will provide confidence to clinicians and medical caresystems. Contd..

11. The Food and Drug Administration(FDA) guidance has to develop a modern regulatory work to make sure that safe and efficient artificial intelligence devicescan efficiently provide topatients. It is also essential to consider the regulatory measures of improvements that providers of AI products are likely to develop the entire product life with the helpof writing a systematicreview. The AI systems will be designed to improve over time, representing a challengeto primary evaluationprocesses. Contd..

12. AI learning is continuous, periodic, and system-wide updates following of clinical significance would be preferred, compared to constant updates that result indrift. Developing the ongoing performance guidelines to calibrate models with human feedback will continually encourage the identification of the performance overtime. Contd..

13. 3. Human Barriers to Adopt AIin Healthcare Even with a highly efficient algorithm that all of the above challenges, human barriers to adoption aresubstantial. it will be essential to maintain a focus on clinical applicability and advance methods for algorithmic interpretability, patient outcomes, and achieve a better understanding of human-computer interactions to ensure that this technology can reach and benefitpatients Contd..

14. 4.Developing aBetter Understanding The human understanding is limited but growing how humans are affected by algorithms in clinical practice by the FDA approval of computer-aided diagnosisformammography. of Humanand Algorithms The computer-aided diagnosis was found to increase the recall rate without improving outcomes significantly. Excessive alerts are known to result in alertfatigue and shown that humans assisted by AIperformed. Contd..

15. Techniques to more represent medical knowledge, facilitate improved interaction and provide an explanation with clinicians meaningfully will only enhance thisperformance. We must continue to gain a better understanding of the evolving relationship between physicians and human-centred AI tools in the live clinicalsectors.

16. Conclusion Recent advancements in artificialintelligence present a huge opportunity to improve the healthcaresector. The transformation of research techniques to effective clinical destruction shows a new frontierfor clinical and machine learningresearch. The prospective and robust clinical evaluation will be essential to ensure that AI systems aresafe. Contd..

17. Using clinical performance metrics that measures of technical accuracy to include the effects of AI affects the quality of health care, the variabilityof healthcare professionals, the productivity of clinical practice, the efficiency and, most importantly, patientoutcomes. Independent data that represent future target populations should be curated to enable the comparison of various algorithms says Pubrica with theirsystematic review writingservice.

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