Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence

Patel, Ankush; Shaker, Nada; Mohanty, Sambit K.; Sharma, Shivani; Gangal, Shivam; Eloy, Catarina; Parwani, Anil V.

doi:10.3390/diagnostics12081778

Cited by 7 publications

(5 citation statements)

References 93 publications

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“…Technological advancements in AI and data science are expected to continue at a rapid pace, with the AI-associated healthcare market projected to grow significantly [109,110]. These advancements are not only revolutionizing healthcare, but also transforming the practice of medicine [75,103,111,112].…”

Section: Future Trends In Explainable Aimentioning

confidence: 99%

The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare

Patel,

Gu,

Esper

et al. 2024

BioMedInformatics

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As artificial intelligence (AI) integrates within the intersecting domains of healthcare and computational biology, developing interpretable models tailored to medical contexts is met with significant challenges. Explainable AI (XAI) is vital for fostering trust and enabling effective use of AI in healthcare, particularly in image-based specialties such as pathology and radiology where adjunctive AI solutions for diagnostic image analysis are increasingly utilized. Overcoming these challenges necessitates interdisciplinary collaboration, essential for advancing XAI to enhance patient care. This commentary underscores the critical role of interdisciplinary conferences in promoting the necessary cross-disciplinary exchange for XAI innovation. A literature review was conducted to identify key challenges, best practices, and case studies related to interdisciplinary collaboration for XAI in healthcare. The distinctive contributions of specialized conferences in fostering dialogue, driving innovation, and influencing research directions were scrutinized. Best practices and recommendations for fostering collaboration, organizing conferences, and achieving targeted XAI solutions were adapted from the literature. By enabling crucial collaborative junctures that drive XAI progress, interdisciplinary conferences integrate diverse insights to produce new ideas, identify knowledge gaps, crystallize solutions, and spur long-term partnerships that generate high-impact research. Thoughtful structuring of these events, such as including sessions focused on theoretical foundations, real-world applications, and standardized evaluation, along with ample networking opportunities, is key to directing varied expertise toward overcoming core challenges. Successful collaborations depend on building mutual understanding and respect, clear communication, defined roles, and a shared commitment to the ethical development of robust, interpretable models. Specialized conferences are essential to shape the future of explainable AI and computational biology, contributing to improved patient outcomes and healthcare innovations. Recognizing the catalytic power of this collaborative model is key to accelerating the innovation and implementation of interpretable AI in medicine.

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Section: Future Trends In Explainable Aimentioning

confidence: 99%

The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare

Patel,

Gu,

Esper

et al. 2024

BioMedInformatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…[96,97] Technological advancements in AI and data science are expected to continue at a rapid pace, with the AI-associated healthcare market projected to grow significantly. [98,99] These advancements are not only revolutionizing healthcare but also transforming the practice of medicine. [46,74,100,101] Future research topics and areas of exploration are likely to focus on the ethical, legal, and societal challenges posed by the rapid advancements in AI.…”

Section: Balancing Explainability and Accuracy/performance In Future ...mentioning

confidence: 99%

The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare

Patel,

Gu,

Esper

et al. 2024

Preprint

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Interdisciplinary conferences play a vital role in advancing explainable Artificial Intelligence (AI) within increasingly intersecting healthcare and computational biology arenas. Challenges result-ing from algorithmic bias and the necessity for domain-specific Explainable AI (XAI) techniques underscore the increasing significance of transparent and interpretable AI systems used in patient care. Collaborative efforts between clinicians, data scientists, and various stakeholders are nec-essary to innovate solutions tailored to healthcare needs. Specialized conferences may foster these interdisciplinary interactions, providing platforms for knowledge exchange, networking, and collaborative partnerships. The legal, ethical, and societal dimensions of medical AI advocate for an integrative approach that aligns technological advancements with patient-centered care val-ues. The role of specialized conferences is therefore essential for shaping future directions in ex-plainable AI and computational biology that contribute to improved patient outcomes and healthcare innovations.

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“…Дополнительно вычислялся ИОК, который представляет собой отношение ИП к ИА [15]. Морфометрическое исследование выполняли с применением электронного аналога окулярной стереометрической сетки Автандилова, содержащей 100 тест-точек и морфометрического программное обеспечение Axiovision ver.4.8 (Carl Zeiss AG, Германия) [20,21]. Статистический анализ начинался с определения типа изучаемых переменных.…”

Section: материалы и методыunclassified

Cell renewal index in the differential diagnosis of atypical small acinar proliferation and adenocarcinoma of the prostate

Dub

2023

jour

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Introduction Singling out such diagnostic category as atypical small acinar proliferation of the prostate (ASAP) determines the necessity of forming additional approaches to differential morphological diagnosis in order to determine adenocarcinoma in time. Cell renewal index (CRI) is actively used to study proliferation and apoptosis in the epithelial cells of hormone-dependent glands, but in prostate pathology this criterion has not been studied.The aim of the study was to characterize the cell renewal index in atypical small acinar proliferation and prostate adenocarcinoma of different degrees of differentiation in order to optimize differential diagnosis.Materials and methods We studied 720 prostate biopsy specimens from 60 patients: Group 1 — 15 patients with ASAP; Group 2 − 15 with adenocarcinoma of high-grade of differentiation; Group 3 − 15 with moderately differentiated adenocarcinoma; Group 4 − 15 with low differentiated adenocarcinoma. We used immunohistochemical method (Ki-67, CPP32) and non-parametric statistical methods of investigation.Results The CRI in ASAP was comparable to that in highly differentiated adenocarcinoma (p=0.5). In moderately differentiated adenocarcinoma the CRI was significantly higher than in ASAP and highly differentiated adenocarcinoma (p=0.004). The greatest increase in CRI was noted in the low-differentiated adenocarcinoma group, which was higher and significantly different from the ASAP (p=0.000), highly differentiated carcinoma (p=0.003) and moderately differentiated prostatic carcinoma (p=0.000) groups.Discussion On the basis of statistical analysis the present study helped to reveal the identity of CRI parameters in ASAP and highly differentiated adenocarcinoma of the prostat, which allows to add a number of morphological criteria for transferring patients from the group of undetermined diagnosis of ASAP to the group of adenocarcinoma. There is a need for further investigation of CRI with multiparametric analysis including investigation of the expression levels of hormones involved in the regulation of cell renewal in the prostate and growth factors associated with them.Conclusion The study allowed to reveal the identity of the CRI in ASAP and highly differentiated adenocarcinoma of the prostate, which can supplement a number of morphological criteria for transferring patients from the group of undeterminate diagnosis of ASAP to the group of adenocarcinoma.

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Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence

Cited by 7 publications

References 93 publications

The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare

The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare

The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare

Cell renewal index in the differential diagnosis of atypical small acinar proliferation and adenocarcinoma of the prostate

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