Table Info

Table 2.

SWOT analysis for AI in lung cancer treatment protocol recommendation

Strengths	Weaknesses	Opportunities	Threats
Precision and accuracy: AI algorithms can analyze complex medical imaging and genetic data to identify subtle patterns for more precise and accurate treatment recommendations.	Algorithm bias: AI models can inherit biases from training data, which may lead to skewed recommendations if the data is not representative of diverse patient populations.	Continuous advancements in personalized medicine: AI has the potential to advance personalized medicine in lung cancer by analyzing individual patient data to tailor treatments.	Ethical and legal concerns: The ethical issues that accompany certain decisions and the potential legal implications of AI errors, pose significant challenges. Human physicians will need to review and sign off on patient charts in the current clinical setting.
Speed of diagnosis: AI can rapidly process and analyze large datasets, significantly reducing time expenditures.	High implementation costs: Developing, testing, and implementing AI systems for lung cancer treatment can be costly, requiring significant investment in technology and clinician expertise.	Integration with emerging technologies: Combining AI with emerging technologies like genomics can lead to a better understanding of lung cancer at the molecular level.	Technological disparities: There may be disparities in access to AI technologies between high and low-resource settings, potentially widening health inequities.
Consistency: AI systems provide consistent recommendations based on learned data, reducing variability in treatment suggestions among different oncologists.	Dependency on data quality: The effectiveness of AI recommendations is highly dependent on the quality and comprehensiveness of the data used, including historical treatment outcomes and patient demographics.	Global reach: AI can extend expert-level lung cancer treatment recommendations to underserved regions, improving outcomes where the access to oncologists is limited.	Resistance from healthcare professionals: There may be resistance to AI recommendations from healthcare professionals who are skeptical of replacing traditional clinical judgment with algorithmic decisions.

Declarations

Author contributions

KD: Conceptualization, Investigation, Data curation, Supervision, Visualization, Software, Resources, Formal analysis. SF: Writing—original draft, Writing—review & editing, Formal analysis. FM: Formal analysis, Conceptualization, Project administration. GL: Methodology, Data curation, Formal analysis, Investigation. JZ: Data curation, Investigation, Validity. YQ: Software, Methodology, Validity.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable for this study, which does not include actual patient data.

Consent to participate

The patient who provided the image for Figure 1 consented to contribute this data in a retrospective study approved by Johns Hopkins University’s Ethics Committee and Institutional Review Board. The related authorization information is as follows: Ethic Committee Name: JHU IRB, Approval Code: IRB-1 (retrospective patient data) Approval Date: 3/18/2020.

Consent to publication

Consent for publication has been obtained.

Availability of data and materials

To request the datasets for this study, please contact the corresponding author.

Funding

Not applicable.

Copyright

References

Key Statistics for Lung Cancer [Internet]. American Cancer Society, Inc.; c2024 [cited 2024 Sep 2]. Available from: https://www.cancer.org/cancer/types/lung-cancer/about/key-statistics.html#:~:text=Lung%20cancer%20(both%20small%20cell,(not%20counting%20skin%20cancer

Lung cancer [Internet]. WHO; c2024 [cited 2024 Aug 31]. Available from: https://www.who.int/news-room/fact-sheets/detail/lung-cancer

Small Cell Lung Cancer vs. Non-small Cell Lung Cancer: What’s the Difference? [Internet]. Rockville: National Foundation for Cancer Research; c2024 [cited 2024 Aug 21]. Available from: https://www.nfcr.org/blog/small-cell-lung-cancer-vs-non-small-cell-lung-cancer-whats-the-difference/

Lemjabbar-Alaoui H, Hassan OU, Yang YW, Buchanan P. Lung cancer: Biology and treatment options. Biochim Biophys Acta. 2015;1856:189–210. [DOI] [PubMed] [PMC]

Gomes RG, Vwalika B, Lee C, Willis A, Sieniek M, Price JT, et al. AI system for fetal ultrasound in low-resource settings. arXiv:2203.10139 [Preprint]. 2022 [cited 2024 Jul 15]. Available from: https://arxiv.org/abs/2203.10139 [DOI]

Chan KS, Zary N. Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review. JMIR Med Educ. 2019;5:e13930. [DOI] [PubMed] [PMC]

Chi EA, Chi G, Tsui CT, Jiang Y, Jarr K, Kulkarni CV, et al. Development and Validation of an Artificial Intelligence System to Optimize Clinician Review of Patient Records. JAMA Netw Open. 2021;4:e2117391. [DOI] [PubMed] [PMC]

Svoboda E. Artificial intelligence is improving the detection of lung cancer. Nature. 2020;587:S20–2. [DOI] [PubMed]

Huang S, Yang J, Shen N, Xu Q, Zhao Q. Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective. Semin Cancer Biol. 2023;89:30–7. [DOI] [PubMed]

10.

Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019;25:954–61. [DOI] [PubMed]

11.

AI Beats Radiologists for Accuracy in Lung Cancer Screening [Internet]. WebMD LLC.; c2024 [cited 2024 Aug 11]. Available from: https://www.medscape.com/viewarticle/913428?form=fpf

12.

The Radiology Labor Shortage [Internet]. Reston: American College of Radiology; c2023 [cited 2024 Sep 3]. Available from: https://www.acr.org/Practice-Management-Quality-Informatics/ACR-Bulletin/Articles/March-2022/The-Radiology-Labor-Shortage

13.

Coccia M. Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence. Technol Soc. 2020;60:101198. [DOI]

14.

Coccia M. Converging Artificial Intelligence and Quantum Technologies: Accelerated Growth Effects in Technological Evolution. Technologies. 2024,12:66. [DOI]

15.

Yang Y, Jin G, Pang Y, Wang W, Zhang H, Tuo G, et al. The diagnostic accuracy of artificial intelligence in thoracic diseases: A protocol for systematic review and meta-analysis. Medicine (Baltimore). 2020;99:e19114. [DOI] [PubMed] [PMC]

16.

Sourlos N, Wang J, Nagaraj Y, van Ooijen P, Vliegenthart R. Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification. Cancers (Basel). 2022;14:3867. [DOI] [PubMed] [PMC]

17.

Tasci E, Zhuge Y, Camphausen K, Krauze AV. Bias and Class Imbalance in Oncologic Data—Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets. Cancers (Basel). 2022;14:2897. [DOI] [PubMed] [PMC]

18.

Parikh RB, Teeple S, Navathe AS. Addressing Bias in Artificial Intelligence in Health Care. JAMA. 2019;322:2377–8. [DOI] [PubMed]

19.

Guo LN, Lee MS, Kassamali B, Mita C, Nambudiri VE. Bias in, bias out: Underreporting and underrepresentation of diverse skin types in machine learning research for skin cancer detection—A scoping review. J Am Acad Dermatol. 2022;87:157–9. [DOI] [PubMed]

20.

Voong KR, Hazell SZ, Fu W, Hu C, Lin CT, Ding K, et al. Relationship Between Prior Radiotherapy and Checkpoint-Inhibitor Pneumonitis in Patients With Advanced Non-Small-Cell Lung Cancer. Clin Lung Cancer. 2019;20:e470–9. [DOI] [PubMed] [PMC]

21.

Larrazabal AJ, Nieto N, Peterson V, Milone DH, Ferrante E. Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proc Natl Acad Sci U S A. 2020;117:12592–4. [DOI] [PubMed] [PMC]

22.

Tsutani Y, Miyata Y, Nakayama H, Okumura S, Adachi S, Yoshimura M, et al. Solid tumor size on high-resolution computed tomography and maximum standardized uptake on positron emission tomography for new clinical T descriptors with T1 lung adenocarcinoma. Ann Oncol. 2013;24:2376–81. [DOI] [PubMed]

23.

Viswanathan VS, Toro P, Corredor G, Mukhopadhyay S, Madabhushi A. The state of the art for artificial intelligence in lung digital pathology. J Pathol. 2022;257:413–29. [DOI] [PubMed] [PMC]

24.

Age and Cancer Risk [Internet]. National Cancer Institute; [cited 2024 Sep 23]. Available from: https://www.cancer.gov/about-cancer/causes-prevention/risk/age

25.

Social determinants of health [Internet]. WHO; c2024 [cited 2024 Aug 15]. Available from: https://www.who.int/health-topics/social-determinants-of-health#tab=tab_1

26.

Lung cancer types [Internet]. The Johns Hopkins University, The Johns Hopkins Hospital, and Johns Hopkins Health System; c2024 [cited 2024 Aug 18]. Available from: https://www.hopkinsmedicine.org/health/conditions-and-diseases/lung-cancer/lung-cancer-types#:~:text=What%20You%20Need%20to%20Know%201%20The%20most,size%2C%20recommended%20treatment%20options%20and%20rate%20of%20metastasis

27.

Carini C, Seyhan AA. Tribulations and future opportunities for artificial intelligence in precision medicine. J Transl Med. 2024;22:411. [DOI] [PubMed] [PMC]

28.

He B, Sun C, Li H, Wang Y, She Y, Zhao M, et al. Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction. Phys Med Biol. 2024;69. [DOI] [PubMed]

29.

Coccia M. Artificial intelligence technology in cancer imaging: Clinical challenges for detection of lung and breast cancer. J Soc Adm Sci. 2019;6:82–98. [DOI]