Evaluation of error detection and treatment recommendations in nucleic acid test reports using ChatGPT models DOI
Wenzheng Han, Chao Wan, Rui Shan

et al.

Clinical Chemistry and Laboratory Medicine (CCLM), Journal Year: 2025, Volume and Issue: unknown

Published: April 18, 2025

Abstract Objectives Accurate medical laboratory reports are essential for delivering high-quality healthcare. Recently, advanced artificial intelligence models, such as those in the ChatGPT series, have shown considerable promise this domain. This study assessed performance of specific GPT models-namely, 4o, o1, and o1 mini-in identifying errors within providing treatment recommendations. Methods In retrospective study, 86 Nucleic acid test report seven upper respiratory tract pathogens were compiled. There 285 from four common error categories intentionally randomly introduced into generated incorrected reports. models tasked with detecting these errors, using three senior scientists (SMLS) interns (MLI) control groups. Additionally, generating accurate reliable recommendations following positive outcomes based on corrected χ2 tests, Kruskal-Wallis Wilcoxon tests used statistical analysis where appropriate. Results comparison SMLS or MLI, accurately detected types, average detection rates 88.9 %(omission), 91.6 % (time sequence), 91.7 (the same individual acted both inspector reviewer). However, rate result input format by was only 51.9 %, indicating a relatively poor aspect. exhibited substantial to almost perfect agreement total (kappa [min, max]: 0.778, 0.837). between MLI moderately lower 0.632, 0.696). When it comes reading all reports, showed obviously reduced time compared (all p<0.001). Notably, our also found GPT-o1 mini model had better consistency identification than model, which that GPT-4o model. The pairwise comparisons model’s outputs across repeated runs 0.912, 0.996). GPT-o1(all significantly outperformed p<0.0001). Conclusions capability some accuracy reliability competent, especially, potentially reducing work hours enhancing clinical decision-making.

Language: Английский

Shaping the future of AI in healthcare through ethics and governance DOI Creative Commons
Rabaï Bouderhem

Humanities and Social Sciences Communications, Journal Year: 2024, Volume and Issue: 11(1)

Published: March 15, 2024

Abstract The purpose of this research is to identify and evaluate the technical, ethical regulatory challenges related use Artificial Intelligence (AI) in healthcare. potential applications AI healthcare seem limitless vary their nature scope, ranging from privacy, research, informed consent, patient autonomy, accountability, health equity, fairness, AI-based diagnostic algorithms care management through automation for specific manual activities reduce paperwork human error. main faced by states regulating were identified, especially legal voids complexities adequate regulation better transparency. A few recommendations made protect data, mitigate risks regulate more efficiently international cooperation adoption harmonized standards under World Health Organization (WHO) line with its constitutional mandate digital public health. European Union (EU) law can serve as a model guidance WHO reform International Regulations (IHR).

Language: Английский

Citations

23

Shaping the Future of Healthcare: Ethical Clinical Challenges and Pathways to Trustworthy AI DOI Open Access
Polat Göktaş, Andrzej Grzybowski

Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(5), P. 1605 - 1605

Published: Feb. 27, 2025

Background/Objectives: Artificial intelligence (AI) is transforming healthcare, enabling advances in diagnostics, treatment optimization, and patient care. Yet, its integration raises ethical, regulatory, societal challenges. Key concerns include data privacy risks, algorithmic bias, regulatory gaps that struggle to keep pace with AI advancements. This study aims synthesize a multidisciplinary framework for trustworthy focusing on transparency, accountability, fairness, sustainability, global collaboration. It moves beyond high-level ethical discussions provide actionable strategies implementing clinical contexts. Methods: A structured literature review was conducted using PubMed, Scopus, Web of Science. Studies were selected based relevance ethics, governance, policy prioritizing peer-reviewed articles, analyses, case studies, guidelines from authoritative sources published within the last decade. The conceptual approach integrates perspectives clinicians, ethicists, policymakers, technologists, offering holistic “ecosystem” view AI. No trials or patient-level interventions conducted. Results: analysis identifies key current governance introduces Regulatory Genome—an adaptive oversight aligned trends Sustainable Development Goals. quantifiable trustworthiness metrics, comparative categories applications, bias mitigation strategies. Additionally, it presents interdisciplinary recommendations aligning deployment environmental sustainability goals. emphasizes measurable standards, multi-stakeholder engagement strategies, partnerships ensure future innovations meet practical healthcare needs. Conclusions: Trustworthy requires more than technical advancements—it demands robust safeguards, proactive regulation, continuous By adopting recommended roadmap, stakeholders can foster responsible innovation, improve outcomes, maintain public trust AI-driven healthcare.

Language: Английский

Citations

3

Revolutionizing healthcare and medicine: The impact of modern technologies for a healthier future—A comprehensive review DOI Creative Commons
Aswin Thacharodi, Prabhakar Singh, Ramu Meenatchi

et al.

Health care science, Journal Year: 2024, Volume and Issue: 3(5), P. 329 - 349

Published: Oct. 1, 2024

Abstract The increasing integration of new technologies is driving a fundamental revolution in the healthcare sector. Developments artificial intelligence (AI), machine learning, and big data analytics have completely transformed diagnosis, treatment, care patients. AI‐powered solutions are enhancing efficiency accuracy delivery by demonstrating exceptional skills personalized medicine, early disease detection, predictive analytics. Furthermore, telemedicine remote patient monitoring systems overcome geographical constraints, offering easy accessible services, particularly underserved areas. Wearable technology, Internet Medical Things, sensor empowered individuals to take an active role tracking managing their health. These devices facilitate real‐time collection, enabling preventive care. Additionally, development 3D printing technology has revolutionized medical field production customized prosthetics, implants, anatomical models, significantly impacting surgical planning treatment strategies. Accepting these advancements holds potential create more patient‐centered, efficient system that emphasizes individualized care, better overall health outcomes. This review's novelty lies exploring how radically transforming industry, paving way for effective all. It highlights capacity modern revolutionize addressing long‐standing challenges improving Although approval use digital advanced analysis face scientific regulatory obstacles, they translational research. as continue evolve, poised alter environment, sustainable, efficient, ecosystem future generations. Innovation across multiple fronts will shape revolutionizing provision healthcare, outcomes, equipping both patients professionals with tools make decisions receive treatment. As develop become integrated into standard practices, probably be accessible, effective, than ever before.

Language: Английский

Citations

15

Decoding the black box: Explainable AI (XAI) for cancer diagnosis, prognosis, and treatment planning-A state-of-the art systematic review DOI

Youssef Alaaeldin Ali Mohamed,

Bee Luan Khoo,

Mohd Shahrimie Mohd Asaari

et al.

International Journal of Medical Informatics, Journal Year: 2024, Volume and Issue: 193, P. 105689 - 105689

Published: Nov. 4, 2024

Language: Английский

Citations

9

Rash Decisions: Improving Pediatrician Skills in Dermatologic Diagnosis DOI

Joel Gupta,

Cathryn Sibbald,

Miriam Weinstein

et al.

The Journal of Pediatrics, Journal Year: 2025, Volume and Issue: 278, P. 114436 - 114436

Published: Jan. 15, 2025

Language: Английский

Citations

1

Bias in adjudication: Investigating the impact of artificial intelligence, media, financial and legal institutions in pursuit of social justice DOI Creative Commons
Kashif Javed, Jianchun Li

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(1), P. e0315270 - e0315270

Published: Jan. 3, 2025

The latest global progress report highlights numerous challenges in achieving justice goals, with bias artificial intelligence (AI) emerging as a significant yet underexplored issue. This paper investigates the role of AI addressing within judicial system to promote equitable social justice. Analyzing weekly data from January 1, 2019, December 31, 2023, through wavelet quantile correlation, this study examines short, medium, and long-term impacts integrating AI, media, international legal influence (ILI), financial institutions (IFI) crucial factors Sustainable Development Goal 16 (SDG-16), which focuses on findings indicate that ILI, IFI can help reduce medium long term, although their effects appear mixed less short term. Our research proposes comprehensive policy framework addresses complexities implementing these technologies system. We conclude successfully requires supportive environment embraces technological innovation, backing, robust regulation prevent potential disruptions could reinforce inequalities, perpetuate structural injustices, exacerbate human rights issues, ultimately leading more biased outcomes

Language: Английский

Citations

1

Transforming Dermatopathology With AI: Addressing Bias, Enhancing Interpretability, and Shaping Future Diagnostics DOI Creative Commons
Diala Ra’Ed Kamal Kakish, Jehad Feras AlSamhori,

Andy Noel Ramirez Fajardo

et al.

Dermatological Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: Jan. 17, 2025

ABSTRACT Background Artificial intelligence (AI) is transforming dermatopathology by enhancing diagnostic accuracy, efficiency, and precision medicine. Despite its promise, challenges such as dataset biases, underrepresentation of diverse populations, limited transparency hinder widespread adoption. Addressing these gaps can set a new standard for equitable patient‐centered care. To evaluate how AI mitigates improves interpretability, promotes inclusivity in while highlighting novel technologies like multimodal models explainable (XAI). Results AI‐driven tools demonstrate significant improvements precision, particularly through that integrate histological, genetic, clinical data. Inclusive frameworks, the Monk scale, advanced segmentation methods effectively address biases. However, “black box” nature AI, ethical concerns about data privacy, access to low‐resource settings remain. Conclusion offers transformative potential dermatopathology, enabling equitable, innovative diagnostics. Overcoming persistent will require collaboration among dermatopathologists, developers, policymakers. By prioritizing inclusivity, transparency, interdisciplinary efforts, redefine global standards foster

Language: Английский

Citations

1

Utility of artificial intelligence in dermatology: Challenges and perspectives DOI Creative Commons
Sheetal Jadhav, Farheen Tafti, Rohit Thorat

et al.

IP Indian Journal of Clinical and Experimental Dermatology, Journal Year: 2025, Volume and Issue: 11(1), P. 1 - 9

Published: Feb. 8, 2025

Medicine is entering a transformative era with disruptive technologies such as virtual reality, genomic prediction, data analytics, personalized medicine, stem cell therapy, 3-D printing, and nanorobotics. Dermatology significantly impacted by these advancements, particularly through artificial intelligence (AI). AI, defined devices performing functions typically requiring human intelligence, plays an increasingly prominent role in healthcare. John McCarthy coined the term AI 1956. In dermatology, aids diagnosis, treatment planning, understanding diseases across communities. Machine learning deep learning, subsets of require extensive datasets robust analysis to improve accuracy performance. AI's integration into dermatology revolutionizing field enabling precision, reducing errors, minimizing staffing needs. tools support dermatologists diagnosing treating various conditions, from psoriasis acne dermatitis ulcers. Convolutional neural networks (CNNs) enhance classification skin lesions, while predictive models optimize strategies based on patient data. extends oncology, where it improves cancer detection image histopathological assessment. Despite its potential, faces challenges quality, representativeness, algorithm transparency, ethical considerations. Addressing biases, standardizing imaging protocols, enhancing human-machine collaboration are crucial for maximizing benefits. holds immense promise offering innovative solutions care diagnostic accuracy. The future includes advancements vision-language models, federated precision medicine approaches. Overcoming related privacy, regulatory standards, model evaluation essential successful clinical practice. Collaborative efforts among stakeholders vital drive progress realize full potential ultimately improving outcomes globally.

Language: Английский

Citations

1

Dermatology and artificial intelligence DOI
W Clark Lambert, Andrzej Grzybowski

Clinics in Dermatology, Journal Year: 2024, Volume and Issue: 42(3), P. 207 - 209

Published: Jan. 4, 2024

Language: Английский

Citations

7

Revolutionizing Healthcare DOI
Jaspreet Kaur

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 272 - 287

Published: April 26, 2024

The healthcare industry is currently experiencing a groundbreakingly revolution as cloud robotics and artificial intelligence (AI) come together. This research examines the synergistic relationship between these two advanced technologies their significant influence on improving patient care. Through utilisation of cloud-based computing power capabilities intelligent robotics, systems can attain unparalleled levels efficiency, accessibility, personalisation. Integrating AI algorithms with robotic enables enhanced diagnosis, treatment planning, real-time monitoring, ultimately resulting in outcomes. chapter present condition technologies, explores instances where they have been effectively put into practice, highlights possible obstacles ethical concerns. In this era transformation, it essential to recognise collaborative designing future patient-centred, data-driven systems.

Language: Английский

Citations

7