AI in biomedical science: innovations, challenges, and ethical perspectives

Aynur Aliyeva

Exploration of Digital Health Technologies ›› 2025, Vol. 3 ›› Issue (1) : 101144

PDF (1131KB)
Exploration of Digital Health Technologies ›› 2025, Vol. 3 ›› Issue (1) :101144 DOI: 10.37349/edht.2025.101144
Letter to the Editor
research-article
AI in biomedical science: innovations, challenges, and ethical perspectives
Author information +
History +
PDF (1131KB)

Abstract

Artificial intelligence (AI) increasingly influences biomedical scientific writing and clinical practice. The recent article by Fornalik et al. (Explor Digit Health Technol. 2024;2:235–48. doi: 10.37349/edht.2024.00024) explores AI’s capabilities, challenges, and ethical considerations in scientific communication, particularly highlighting tools like ChatGPT and Penelope.ai. This commentary aims to reflect on and expand the key themes presented by Fornalik et al. (Explor Digit Health Technol. 2024;2:235–48. doi: 10.37349/edht.2024.00024), emphasizing AI’s role in auditory healthcare, particularly in otolaryngology and auditory rehabilitation. The discussion is based on a critical review and synthesis of recent literature on AI applications in scientific writing and auditory healthcare. Key technologies such as generative AI platforms, machine learning algorithms, and mobile-based auditory training systems are highlighted. AI has shown promising results in enhancing manuscript preparation, literature synthesis, and peer review workflows. In clinical practice, adaptive AI models have improved cochlear implant programming, leading to up to 30% gains in speech perception accuracy. Mobile apps and telehealth platforms using AI have also improved listening effort, communication confidence, and access to care in remote settings. However, limitations include data privacy concerns, lack of population diversity in datasets, and the need for clinician oversight. AI presents transformative opportunities across biomedical science and healthcare. To ensure its responsible use, interdisciplinary collaboration among clinicians, researchers, ethicists, and technologists is essential. Such collaboration can help develop ethical frameworks that enhance innovation while safeguarding patient well-being and scientific integrity.

Keywords

Artificial intelligence / biomedical science / auditory rehabilitation / neurotechnology / ChatGPT / healthcare ethics

Cite this article

Download citation ▾
Aynur Aliyeva. AI in biomedical science: innovations, challenges, and ethical perspectives. Exploration of Digital Health Technologies, 2025, 3 (1) : 101144 DOI:10.37349/edht.2025.101144

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Fornalik M, Makuch M, Lemanska A, Moska S, Wiczewska M, Anderko I, et al. Rise of the machines: trends and challenges of implementing AI in biomedical scientific writing. Explor Digit Health Technol. 2024; 2:235-48.

[2]

Aliyeva A, Sari E. Be or Not to Be With ChatGPT? Cureus. 2023; 15:e48366.

[3]

Aliyeva A. “Bot or Not”: Turing Problem in Otolaryngology. Cureus. 2023; 15:e48170.

[4]

Diniz-Freitas M, López-Pintor RM, Santos-Silva AR, Warnakulasuriya S, Diz-Dios P. Assessing the accuracy and readability of ChatGPT-4 and Gemini in answering oral cancer queries-an exploratory study. Explor Digit Health Technol. 2024; 2:334-45.

[5]

Aliyeva A, Alaskarov E, Sari E. Postoperative Management of Tympanoplasty with ChatGPT-4.0. J Int Adv Otol. 2025; 21:1-6.

[6]

Ding K, Forbes S, Ma F, Luo G, Zhou J, Qi Y. AI bias in lung cancer radiotherapy. Explor Digit Health Technol. 2024; 2:302-12.

[7]

Han JS, Lim JH, Kim Y, Aliyeva A, Seo J, Lee J, et al. Hearing Rehabilitation With a Chat-Based Mobile Auditory Training Program in Experienced Hearing Aid Users: Prospective Randomized Controlled Study. JMIR Mhealth Uhealth. 2024; 12:e50292.

[8]

Borjigin A, Kokkinakis K, Bharadwaj HM, Stohl JS. Deep learning restores speech intelligibility in multi-talker interference for cochlear implant users. Sci Rep. 2024; 14:13241.

[9]

Rajamäki J. Digital Twin Technology training and research in health higher education: a review. Explor Digit Health Technol. 2024; 2:188-201.

[10]

Deo N, Nawaz FA, du Toit C, Tran T, Mamillapalli C, Mathur P, et al. HUMANE: Harmonious Understanding of Machine Learning Analytics Network-global consensus for research on artificial intelligence in medicine. Explor Digit Health Technol. 2024; 2:157-66.

[11]

Ueda D, Kakinuma T, Fujita S, Kamagata K, Fushimi Y, Ito R, et al. Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol. 2024; 42:3-15.

[12]

Williamson SM, Prybutok V. Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare. Appl Sci. 2024; 14:675.

[13]

Lee EE, Torous J, De Choudhury M, Depp CA, Graham SA, Kim HC, et al. Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom. Biol Psychiatry Cogn Neurosci Neuroimaging. 2021; 6:856-64.

[14]

Harishbhai Tilala M, Kumar Chenchala P, Choppadandi A, Kaur J, Naguri S, Saoji R, et al. Ethical Considerations in the Use of Artificial Intelligence and Machine Learning in Health Care: A Comprehensive Review. Cureus. 2024; 16:e62443.

PDF (1131KB)

0

Accesses

0

Citation

Detail

Sections
Recommended

/

〈 〉